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06 10.22034/zagros.2021.0622.0502 https://rcsj.ir/user/articles/144 Farzanegan Pub mEDRA 01 01 144 zagros Publisher IR 01 144 JB 32 1 4 01 202605 7 10 01 [1] قره نژاد سحر.لزوم حفظ مشتریان بیمه با استفاده از ابزارهای داده کاوی،دانشجوي كارشناسي ارشد مديريت فناوري اطلاعات، تازه های جهان بیمه. شماره 150 و151. [2] Abbasi, S. O., Nejatian, S., Parvin, H., Rezaie, V., & Bagherifard, K. (2019). Clustering ensemble selection considering quality and diversity. Artificial Intelligence Review, 52(2), 1311-1340. [3] Yu, Z., Chen, H., You, J., Han, G., & Li, L. (2013). Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data. IEEE/ACM transactions on computational biology and bioinformatics, 10(3), 657-670. [4] Akbari, E., Dahlan, H. M., Ibrahim, R., & Alizadeh, H. (2015). Hierarchical cluster ensemble selection. Engineering Applications of Artificial Intelligence, 39, 146-156. [5] Sivakumar, A., & Gunasundari, R. (2017). A Survey on Data Preprocessing Techniques for Bioinformatics and Web Usage Mining. International Journal of Pure and Applied Mathematics, 117(20), 785-794. [6] Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. [7] Mirza, S., Mittal, S., & Zaman, M. (2016). A Review of Data Mining Literature. International Journal of Computer Science and Information Security (IJCSIS), 14(11). [8] Sivakumar, A., & Gunasundari, R. (2017). A Survey on Data Preprocessing Techniques for Bioinformatics and Web Usage Mining. International Journal of Pure and Applied Mathematics, 117(20), 785-794. [9] Kuwil, F. H., Shaar, F., Topcu, A. E., & Murtagh, F. (2019). A new data clustering algorithm based on critical distance methodology. Expert Systems with Applications, 129, 296-310. [10] Kleinberg, J. M. (2003). An impossibility theorem for clustering. In Advances in neural information processing systems (pp. 463-470). [11] García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2010). A review of robust clustering methods. Advances in Data Analysis and Classification, 4(2-3), 89-109. [12] Coretto, P., & Hennig, C. (2010). A simulation study to compare robust clustering methods based on mixtures. Advances in Data Analysis and Classification, 4(2-3), 111-135. [13] Fred, A. L., & Jain, A. K. (2002, August). Data clustering using evidence accumulation. In Object recognition supported by user interaction for service robots (Vol. 4, pp. 276-280). IEEE. [14] Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, 3(Dec), 583-617. [15] Topchy, A., Jain, A. K., & Punch, W. (2003, November). Combining multiple weak clusterings. In Third IEEE international conference on data mining (pp. 331-338). IEEE. [16] Gullo, F., Tagarelli, A., & Greco, S. (2009, April). Diversity-based weighting schemes for clustering ensembles. In Proceedings of the 2009 SIAM International Conference on Data Mining (pp. 437-448). Society for Industrial and Applied Mathematics. [17] Fred, A., & Lourenço, A. (2008). Cluster ensemble methods: from single clusterings to combined solutions. In Supervised and unsupervised ensemble methods and their applications (pp. 3-30). Springer, Berlin, Heidelberg. [18] Mirzaei, A. (2009). Combining hierarchical clusterings with emphasis on retaining the structural contents of the base clusterings. Computer Engineering & IT Department, Amir-kabir University of Technology, Tehran. [19] Friedman, J. H., & Meulman, J. J. (2004). Clustering objects on subsets of attributes (with discussion). Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(4), 815-849. [20] Domeniconi, C., Gunopulos, D., Ma, S., Yan, B., Al-Razgan, M., & Papadopoulos, D. (2007). Locally adaptive metrics for clustering high dimensional data. Data Mining and Knowledge Discovery, 14(1), 63-97. [21] Al-Razgan, M., & Domeniconi, C. (2006, April). Weighted clustering ensembles. In Proceedings of the 2006 SIAM International Conference on Data Mining (pp. 258-269). Society for Industrial and Applied Mathematics. [22] Domeniconi, C., & Al-Razgan, M. (2009). Weighted cluster ensembles: Methods and analysis. ACM Transactions on Knowledge Discovery from Data (TKDD), 2(4), 1-40. [23] Li, T., & Ding, C. (2008, April). Weighted consensus clustering. In Proceedings of the 2008 SIAM International Conference on Data Mining (pp. 798-809). Society for Industrial and Applied Mathematics. [24] Gullo, F., Tagarelli, A., & Greco, S. (2009, April). Diversity-based weighting schemes for clustering ensembles. In Proceedings of the 2009 SIAM International Conference on Data Mining (pp. 437-448). Society for Industrial and Applied Mathematics. [25] Huang, D., Lai, J., & Wang, C. D. (2016). Ensemble clustering using factor graph. Pattern Recognition, 50, 131-142. [26] Liu, H., Liu, T., Wu, J., Tao, D., & Fu, Y. (2015, August). Spectral ensemble clustering. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 715-724). [27] Ren, Y., Domeniconi, C., Zhang, G., & Yu, G. (2017). Weighted-object ensemble clustering: methods and analysis. Knowledge and Information Systems, 51(2), 661-689. [28] Mojarad, M., Nejatian, S., Parvin, H., & Mohammadpoor, M. (2019). A fuzzy clustering ensemble based on cluster clustering and iterative fusion of base clusters. Applied Intelligence, 49(7), 2567-2581. [29] Hailin, L., & Miao, W. (2020). Fuzzy clustering based on feature weights for multivariate time series. Knowledge-Based Systems, 105907. [30] Soufiane, K., Imene, H., Manel, A., & Tarek, K. M. (2019, March). Clustering Ensemble Approach Based on Incremental Learning. In Proceedings of the 9th International Conference on Information Systems and Technologies (pp. 1-7). [31] Liang, W., Zhang, Y., Xu, J., & Lin, D. (2019). Optimization of Basic Clustering for Ensemble Clustering: An Information-Theoretic Perspective. IEEE Access, 7, 179048-179062. [32] Yang, H., Peng, H., Zhu, J., & Nie, F. (2020). Co-Clustering Ensemble Based on Bilateral K-Means Algorithm. IEEE Access, 8, 51285-51294. [33] Latifi Pakdehi, A., & Daneshpour, N. (2019). Cluster ensemble selection using voting. Signal and Data Processing, 15(4), 17-30. [34] Zhang, M. (2019). Weighted Clustering Ensemble: A Review. arXiv preprint arXiv:1910.02433. [35] Harakawa, R., Takimura, S., Ogawa, T., Haseyama, M., & Iwahashi, M. (2019). Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation. IEEE Access, 7, 116207-116217. [36] Vahidi Ferdosi, S., & Amirkhani, H. (2020). Weighted Ensemble Clustering for Increasing the Accuracy of the Final Clustering. Signal and Data Processing, 17(2), 100-85. [37] Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artificial Intelligence, 25(03), 337-372. [38] Topchy, A., Jain, A. K., & Punch, W. (2003, November). Combining multiple weak clusterings. In Third IEEE international conference on data mining (pp. 331-338). IEEE. [39] Fred, A. L., & Jain, A. K. (2005). Combining multiple clusterings using evidence accumulation. IEEE transactions on pattern analysis and machine intelligence, 27(6), 835-850. [40] Azimi, J., & Fern, X. Z. (2009, July). Adaptive cluster ensemble selection. In Ijcai (Vol. 9, pp. 992-997). [41] Bai, L., Liang, J., & Cao, F. (2020). A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters. Information Fusion, 61, 36-47. 1 A01 رقیه مجد آبادی، علیرضا دهقانی رقیه مجد آبادی، علیرضا دهقانی C. رقیه مجد آبادی، علیرضا دهقانی 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2021.0725.0039 https://rcsj.ir/user/articles/145 Farzanegan Pub mEDRA 01 01 145 zagros Publisher IR 01 145 JB 32 1 4 01 202605 7 10 01 [1] شیرازی، ‌حسین.، فرشچی، ‌سیدمحمدرضا. 1393. ارائه یک روش جدید برای شناسایی بدافزارها در سطح مجازی ساز در ماشین های مجازی. پدافند الکترونیکی و سایبری: 7(2): 23-34. [2] رنجی، هادی، و پارسا، سعید. 1397. شناسائی بدافزارها با استفاده از تصویرسازی. فصلنامه پدافند غیرعامل, 9(2): 101-95. [3] Gibert, D., 2016. Convolutional neural networks for malware classification. University Rovira i Virgili, Tarragona, Spain. [4] Gupta, A., Kuppili, P., Akella, A. and Barford, P., 2009, January. An empirical study of malware evolution. In 2009 First International Communication Systems and Networks and Workshops (pp. 1-10). IEEE. [5] Ma, J., Dunagan, J., Wang, H.J., Savage, S. and Voelker, G.M., 2006, October. Finding diversity in remote code injection exploits. In Proceedings of the 6th ACM SIGCOMM conference on Internet measurement (pp. 53-64). [6] Mercaldo, F., Di Sorbo, A., Visaggio, C.A., Cimitile, A. and Martinelli, F., 2018. An exploratory study on the evolution of Android malware quality. Journal of Software: Evolution and Process, 30(11), p.e1978. [7] Ouellette, J., Pfeffer, A. and Lakhotia, A., 2013, October. Countering malware evolution using cloud-based learning. In 2013 8th International Conference on Malicious and Unwanted Software:" The Americas"(MALWARE) (pp. 85-94). IEEE. [8] Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E. and Ahmadi, M., 2018. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135. [9] WebSite, 2020. Available at: https://sarvamblog.blogspot.com/2014/08/supervised-classification-with-k-fold.html [10] Zakaria, M., Al-Shebany, M. and Sarhan, S., 2014. Artificial neural network: a brief overview. Int J Eng Res Appl, 4, pp.7-12. [11] Zha, H., He, X., Ding, C., Gu, M. and Simon, H.D., 2002. Spectral relaxation for k-means clustering. In Advances in neural information processing systems (pp. 1057-1064). [12] Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E. and Ahmadi, M., 2018. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135. [13] Saxe, J. and Berlin, K., 2015, October. Deep neural network based malware detection using two dimensional binary program features. In 2015 10th International Conference on Malicious and Unwanted Software (MALWARE) (pp. 11-20). IEEE. [14] Kramer, S. and Bradfield, J.C., 2010. A general definition of malware. Journal in computer virology, 6(2), pp.105-114. [15] Forrest, S., Hofmeyr, S.A., Somayaji, A. and Longstaff, T.A., 1996, May. A sense of self for unix processes. In Proceedings 1996 IEEE Symposium on Security and Privacy (pp. 120-128). IEEE. [16] Wang, K. and Stolfo, S.J., 2004, September. Anomalous payload-based network intrusion detection. In International workshop on recent advances in intrusion detection (pp. 203-222). Springer, Berlin, Heidelberg. [17] Kalash, M., Rochan, M., Mohammed, N., Bruce, N.D., Wang, Y. and Iqbal, F., 2018, February. Malware classification with deep convolutional neural networks. In 2018 9th IFIP international conference on new technologies, mobility and security (NTMS) (pp. 1-5). IEEE. [18] Makandar, A. and Patrot, A., 2015, December. Malware analysis and classification using artificial neural network. In 2015 International conference on trends in automation, communications and computing technology (I-TACT-15) (pp. 1-6). IEEE. [19] Idika, N. and Mathur, A.P., 2007. A survey of malware detection techniques. Purdue University, 48, pp.2007-2. [20] Elhadi, A.A., Maarof, M.A. and Osman, A.H., 2012. Malware detection based on hybrid signature behaviour application programming interface call graph. American Journal of Applied Sciences, 9(3), p.283. [21] Tang, A., Sethumadhavan, S. and Stolfo, S.J., 2014, September. Unsupervised anomaly-based malware detection using hardware features. In International Workshop on Recent Advances in Intrusion Detection (pp. 109-129). Springer, Cham. [22] Landage, J. and Wankhade, M.P., 2013. Malware and malware detection techniques: A survey. International Journal of Engineering Research and Technology (IJERT), 2(12), pp.2278-0181. 1 A01 معصومه مرادی، مجتبی صالحی معصومه مرادی، مجتبی صالحی C. معصومه مرادی، مجتبی صالحی 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2021.0725.0129 https://rcsj.ir/user/articles/146 Farzanegan Pub mEDRA 01 01 146 zagros Publisher IR 01 146 JB 32 1 4 01 202605 7 10 01 [1] Ashton K, et al. (2009) That ‘internet of things’ thing. RFID J 22(7):97–114 [2] Ali ZH, Ali HA, Badawy MM (2015) Intenet of things (IoT): definitions, challenges and recent research directions. Int J Comput Appl 128(1):37–47 [3] HaddadPajouh H, Dehghantanha A, Parizi RM, Aledhari M, Karimipour H (2019) A survey on internet of things security: requirements, challenges, and solutions. Internet of Things 3:100–129 [4] da Costa CA, Pasluosta CF, Eskofier B, da Silva DB, da Rosa Righi R (2018) Internet of health things: toward intelligent vital signs monitoring in hospital wards. Artif Intell Med 89:61–69 [5] Islam SMR, Kwak D, Kabir MDH, Hossain M, Kwak K-S (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708 [6] Hu F, Xie D, Shen S (2013) On the application of the internet of things in the field of medical and health care. In: 2013 IEEE International Conference on Green Computing and Communications an IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. IEEE, pp 2053–2058 [7] Qi J, Yang P, Min G, Amft O, Dong F, Xu L (2017) Advanced internet of things for personalized healthcare systems: a survey. Pervasive Mob Comput 41:132–149 [8] IoT in healthcare market. (2020) https://www.marketsandmarkets.com/Market-Reports/iot-healthcaremarket-160082804.html. Accessed June 29, 2020 [9] Lovelace Jr B (2020) Scientists say the coronavirus is at least as deadly as the 1918 flu pandemic. https://www.cnbc.com/berkeley-lovelace-jr/. Accessed September 5, 2020 [10] WHO (2020) Coronavirus disease (COVID-19). https://bit.ly/2ZU5x08. Accessed July 09, 2020 [11] Symptoms of coronavirus. (2020) https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html. Accessed June 26, 2020 [12] CDC (2020) Quarantine if you might be sick. https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine.html. Accessed July 04, 2020 [13] Wang W, Tang J, Wei F (2020) Updated understanding of the outbreak of 2019 novel coronavirus(2019-nCoV) in Wuhan, China. J Med Virol 92(4):441–447 [14] Peeri NC, Shrestha N, Rahman MS, Zaki R, Tan Z, Bibi S, Baghbanzadeh M, Aghamohammadi N, Zhang W, Haque U (2020) The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?. International Journal of Epidemiology [15] Singh RP, Javaid M, Haleem A, Suman R (2020) Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews [16] Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26(4):459–461 [17] To KK-W, Tsang OT-Y, Yip CC-Y, Chan K-H, Wu T-C, Chan JM-C, Leung W-S, Chik TS-H, Choi CY-C, Kandamby DH, et al. (2020) Consistent detection of 2019 novel coronavirus in saliva. Clin Infect Dis [18] Xing Y, Mo P, Xiao Y, Zhao O, Zhang Y, Wang F (2020) Post-discharge surveillance and positive virus detection in two medical staff recovered from coronavirus disease 2019 (COVID-19), China, January to February 2020. Eurosurveillance 25(10):2000191 [19] Zhang SX, Wang Y, Rauch A, Wei F (2020) Unprecedented disruption of lives and work: health, distress and life satisfaction of working adults in China one month into the COVID-19 outbreak. Psychiatry Res 112958 [20] Christaki E (2015) New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 6(6):558–565 [21] Phelan AL, Katz R, Gostin LO (2020) The novel coronavirus originating in Wuhan, China: challenges for global health governance. Jama 323(8):709–710 [22] Nussbaumer-Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, Wagner G, Siebert U, Christof C, Zachariah C, et al. (2020) Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. Cochrane Database Syst Rev (4( [23] Rahman MS, Peeri NC, Shrestha N, Zaki R, Haque U, Ab Hamid SH (2020) Defending against the novel coronavirus (COVID-19) outbreak: how can the internet of things (IoT) help to save the world?. Health Policy and Technology [24] CDC (2020) Prevent the spread of COVID-19 if you are sick. https://bit.ly/3hj7tq1. Accessed July 20, 2020 [25] Lee BY (2020) Can you get COVID-19 coronavirus twice? here is an update on reinfection. https://www.forbes.com/sites/brucelee/2020/07/19/can-you-get-covid-19-coronavirus-twice-here-isan-update-on-reinfection/#6499ed737cbf. Accessed July 20, 2020 [26] Nasajpour M, Pouriyeh S, Parizi RM, Dorodchi M, Valero M, Arabnia HR. Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study. J Healthc Inform Res. 2020 Nov 12:1-40. doi: 10.1007/s41666-020-00080-6. Epub ahead of print. PMID: 33204938; PMCID: PMC7659418. [27] Singh RP, Javaid M, Haleem A, Suman R. Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul-Aug;14(4):521-524. doi: 10.1016/j.dsx.2020.04.041. Epub 2020 May 5. PMID: 32388333; PMCID: PMC7198990. [28] Swati Swayamsiddha, Chandana Mohanty, Application of cognitive Internet of Medical Things for COVID-19 pandemic,Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Volume 14, Issue 5,2020, Pages 911-915, ISSN 1871-4021 [29] Taiwo, Olutosin & Ezugwu, Absalom. (2020). Smart healthcare support for remote patient monitoring during covid-19 quarantine. Informatics in Medicine Unlocked. 20. 10.1016/j.imu.2020.100428. [30] Garrett W, Christopher P, Nisha R, Gabriel de la C, Shivam G, Sepehr N, Bryan M, Maureen S-E, Matthew TE, Diane CJ. Robot-enabled support of daily activities in smart home environments. Cognit Syst Res 2019;54:258–72. 2018. [31] Rozita T, Salah AA, Kok CW, Mok HV. Smart GSM based home automation system. In: IEEE conference on systems, process & control (ICSPC2013). Malaysia: Kuala Lumpur; 2013. 2013. [32] Hamed B. Design & implementation of smart house control using LabVIEW. Int J Soft Comput Eng 2012;1(6):98–106. [33] Himanshu S, Vishal P, Vedant K, Venkanna U. IoT based smart home automation system using sensor node. In: 4th international conference on recent advances in information technology. RAIT-2018); 2018. [34] Suyang Z, Zhi W, Jianing L, Xiao-ping Z. Real-time energy control approach for smart home energy management system. Elec Power Compon Syst 2014;42(3–4): 315–26. [35] Mohammad HA, Chad D. Design and implementation of an IoT-based smart home security system. International Journal of Networked and Distributed Computing 2019;7(2):85–92. [36] Lin Y, Yanhong G, Wenfeng L, Wenbi R. A home mobile healthcare system for wheelchair users. In: IEEE 18th conference on computer supported cooperative work in design. Hsinchu; 2014. [37] Catarinucci L, Donno Dd, Mainetti L, Palano L, Patrono L, Stefanizzi ML, Tarricone L. An IoT-aware architecture for smart healthcare systems. IEEE Internet of Things Journal 2015;2(6):515–26. [38] Rasch K, Li F, Sehic S, Ayani R, Dustdar S. Context-driven personalized service discovery in pervasive environments. World Wide Web 2011;14:295–319. [39] Min C, Sergio G, Victor L, Qian Z, Ming L. A 2G-RFID-based E-healthcare system. February. IEEE Wireless Communication; 2010. p. 37–43. [40] Dey AK. Understanding and using context. Personal Ubiquitous Comput 2001;5(1): 4–7. [41] School HM. Tips to measure your blood pressure correctly. Harvard Health Publishing 2020 [Online]. Available, https://www.health.harvard.edu/heart-hea lth/tips-to-measure-your-blood-pressure-correctly. Accessed 6 August. [42] Katharina R, Fei L, Sanjin S, Rassul A, Schahram D. Context-Driven personalized service discovery in pervasive environments. World Wide Web 2011;(14):295–319. 2011. [43] R.P. Singh, M. Javaid, A. Haleem, R. Suman, Internet of things (IoT) applications to fight against COVID-19 pandemic, Diabetes Metab Syndr 2020; 14(4): 521-524. [44] D.S.W. Ting, L. Carin, V. Dzau, T.Y. Wong, Digital technology and COVID-19,Nat Med 2020;26(4): 459-461. 45. R. Vaishya, M. Javaid, I.H. Khan, A. Haleem, Artificial Intelligence (AI) applications for COVID-19 pandemic, Diabetes Metab Syndr 2020; 14(4): 337-339. [45] M. Javaid, A. Haleem, R. Vaishya, S. Bahl, R. Suman, A. Vaish, Industry 4.0 technologies and their applications in fighting COVID-19 pandemic, Diabetes Metab Syndr 2020; 14(4):419-422. [46] E. W., I. M., Multiband spectrum sensing and resource allocation for IoT in cognitive 5G networks, IEEE Internet of Things Journal 2017; 5(1):150-63. [47] T. Yang, M. Gentile, C.F. Shen, C.M. Cheng, Combining Point-of-Care Diagnostics and Internet of Medical Things (IoMT) to Combat the COVID-19 Pandemic, Diagnostics (Basel) 2020; 10(4). [48] C. V Bouten, K. T. Koekkoek, M. Verduin, R. Kodde, and J. D.Janssen, “A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity.,” IEEE Trans. Biomed. Eng., vol. 44, no. 3, pp. 136–147, 1997 [49] H. Dejnabadi, B. M. Jolles, and K. Aminian, “A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes,” IEEE Trans. Biomed. Eng., vol. 52, no. 8, pp. 1478–1484, 2005 [50] a Moncada-Torres, K. Leuenberger, R. Gonzenbach, a Luft, and R. Gassert, “Activity classification based on inertial and barometric pressure sensors at different anatomical locations,” Physiol. Meas., vol. 35, no. 7, pp. 1245–1263, 2014 [51] M. Shoaib, S. Bosch, O. Incel, H. Scholten, and P. Havinga, “A Survey of Online Activity Recognition Using Mobile Phones,” Sensors, vol. 15, no. 1, pp. 2059–2085, 2015 [52] L. Liao, D. Fox, and H. Kautz, “Hierarchical Conditional Random Fields for GPS-Based Activity Recognition,” Star, vol. 28, pp. 487– 506, 2007. [53] R. Davies, L. Galway, C. Nugent, C. Jamison, R. Gawley, P. McCullagh, H. Zhang, and N. Black, “A Platform for Self-Management Supported by Assistive, Rehabilitation and Telecare Technologies,” Proc. 5th Int. ICST Conf. Pervasive Comput. Technol. Healthc., pp. 458–460, 2011 [54] T. Pawar, N. S. Anantakrishnan, S. Chaudhuri, and S. P. Duttagupta, “Impact of Ambulation in Wearable-ECG,” Ann. Biomed. Eng., vol. 36, no. 9, pp. 1547–1557, 2008. [55] D. Wensley and M. Silverman, “Peak Flow Monitoring for Guided Self-management A Randomized Controlled Trial,” vol. 170, pp. 606–612, 2004. [56] A. Bulling, J. a. Ward, and H. Gellersen, “Multimodal recognition of reading activity in transit using body-worn sensors,” vol. 9, no. 1, pp. 1–21, 2012. [57] F. Sun, C. Kuo, H. Cheng, S. Buthpitiya, P. Collins, and M. Griss, “Activity-aware Mental Stress Detection Using Physiological Sensors,” Mob. Comput. Appl. Serv., vol. 76, pp. 1–20, 2012. [58] A. I. Journal, E. Berry, N. Kapur, L. Williams, S. Hodges, G. Smyth, J. Srinivasan, R. Smith, B. Wilson, E. Berry, N. Kapur, L. Williams, S. Hodges, G. Smyth, J. Srinivasan, R. Smith, B. Wilson, and K. W. The, “The use of a wearable camera , SenseCam , as a pictorial diary to improve autobiographical memory in a patient with limbic encephalitis¬: A preliminary report,” vol. 2011, no. April 2016, 2011. [59] A. R. Doherty, N. Caprani, C. Ó. Conaire, V. Kalnikaite, C. Gurrin, A. F. Smeaton, and N. E. O. Connor, “Computers in Human Behavior Passively recognising human activities through lifelogging,” Comput. Human Behav., vol. 27, no. 5, pp. 1948–1958, 2011. [60] C. Sugimoto and R. Kohno, “Wireless Sensing System for Healthcare Monitoring Thermal Physiological State and Recognizing Behavior,” pp. 285–291, 2011. [61] P. De Toledo and A. Sanchis, “Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors ˜ *,” pp. 5460–5477, 2013. [62] A. Sixsmith and N. Johnson, “A Smart Sensor to,” 2004. [63] H. Jong, S. Hee, K. Ha, H. Chul, W. Chung, J. Young, Y. Chang, and D. Hyun, “Ubiquitous healthcare service using Zigbee and mobile phone for elderly patients,” vol. 8, pp. 193–198, 2008 [64] P. Yang, W. Wu, M. Moniri, and C. C. Chibelushi, “Efficient object localization using sparsely distributed passive RFID tags,” IEEE Trans. Ind. Electron., vol. 60, no. 12, pp. 5914–5924, 2013. [65] R. S. Sangwan, R. G. Qiu, S. K. Member, and D. Jessen, “Using RFID Tags for Tracking Patients , Charts and Medical Equipment within an Integrated Health Delivery Network,” pp. 70–74. [66] J. Rafferty, C. N.-I. Member, L. C.-I. Member, J. Qi, R. Dutton, A. Zirk, L. T. Boye, M. Kohn, and R. Hellman, “NFC based provisioning of instructional videos to assist with instrumental activities of daily living,” pp. 4131–4134, 2014. 1 A01 زهرا نخعی راد زهرا نخعی راد C. زهرا نخعی راد 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2021.0728.0050 https://rcsj.ir/user/articles/147 Farzanegan Pub mEDRA 01 01 147 zagros Publisher IR 01 147 JB 32 1 4 01 202605 7 10 01 [1] D. Pal, T. Triyason, and S. Funikul, "Smart homes and quality of life for the elderly: a systematic review," in 2017 IEEE International Symposium on Multimedia (ISM), 2017, pp. 413-419. [2] M. Yu, A. Rhuma, S. M. Naqvi, L. Wang, and J. Chambers, "A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment," IEEE transactions on information technology in biomedicine, vol. 16, pp. 1274-1286, 2012. [3] S. Majumder, E. Aghayi, M. Noferesti, H. Memarzadeh-Tehran, T. Mondal, Z. Pang, et al., "Smart homes for elderly healthcare—Recent advances and research challenges," Sensors, vol. 17, p. 2496, 2017. [4] M. Chan, E. Campo, D. Estève, and J.-Y. Fourniols, "Smart homes—current features and future perspectives," Maturitas, vol. 64, pp. 90-97, 2009. [5] N. K. Suryadevara, S. C. Mukhopadhyay, R. Wang, and R. Rayudu, "Forecasting the behavior of an elderly using wireless sensors data in a smart home," Engineering Applications of Artificial Intelligence, vol. 26, pp. 2641-2652, 2013. [6] R. S. Ransing and M. Rajput, "Smart home for elderly care, based on Wireless Sensor Network," in 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE), 2015, pp. 1-5. [7] L. C. De Silva, C. Morikawa, and I. M. Petra, "State of the art of smart homes," Engineering Applications of Artificial Intelligence, vol. 25, pp. 1313-1321, 2012. [8] M. Fahim, I. Fatima, S. Lee, and Y.-K. Lee, "Daily life activity tracking application for smart homes using android smartphone," in 2012 14th International conference on advanced communication technology (ICACT), 2012, pp. 241-245. [9] S. ur Rehman and V. Gruhn, "An approach to secure smart homes in cyber-physical systems/Internet-of-Things," in 2018 Fifth International Conference on Software Defined Systems (SDS), 2018, pp. 126-129. [10] D. Pal, B. Papasratorn, W. Chutimaskul, and S. Funilkul, "Embracing the smart-home revolution in Asia by the elderly: An end-user negative perception modeling," IEEE Access, vol. 7, pp. 38535-38549, 2019. [11] N. Fernando, F. T. C. Tan, R. Vasa, K. Mouzaki, and I. Aitken, "Examining digital assisted living: towards a case study of smart homes for the elderly," 2016. [12] D. Ding, R. A. Cooper, P. F. Pasquina, and L. Fici-Pasquina, "Sensor technology for smart homes," Maturitas, vol. 69, pp. 131-136, 2011. [13] M. Hamill, V. Young, J. Boger, and A. Mihailidis, "Development of an automated speech recognition interface for personal emergency response systems," Journal of NeuroEngineering and Rehabilitation, vol. 6, pp. 1-11, 2009. [14] Z. Callejas and R. López-Cózar, "Designing smart home interfaces for the elderly," ACM SIGACCESS Accessibility and Computing, pp. 10-16, 2009. [15] T. Koskela and K. Väänänen-Vainio-Mattila, "Evolution towards smart home environments: empirical evaluation of three user interfaces," Personal and Ubiquitous Computing, vol. 8, pp. 234-240, 2004. [16] D. Pal, S. Funilkul, V. Vanijja, and B. Papasratorn, "Analyzing the elderly users’ adoption of smart-home services," IEEE Access, vol. 6, pp. 51238-51252, 2018. [17] R. Sokullu, M. A. Akkaş, and E. Demir, "IoT supported smart home for the elderly," Internet of Things, vol. 11, p. 100239, 2020. [18] K. Nisar, A. A. A. Ibrahim, L. Wu, A. Adamov, and M. J. Deen, "Smart home for elderly living using Wireless Sensor Networks and an Android application," in 2016 IEEE 10th international conference on application of information and communication technologies (AICT), 2016, pp. 1-8. [19] R. Gnanavel, P. Anjana, K. Nappinnai, and N. P. Sahari, "Smart home system using a wireless sensor network for elderly care," in 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM), 2016, pp. 51-55. [20] Y. Charlon, W. Bourennane, F. Bettahar, and E. Campo, "Activity monitoring system for elderly in a context of smart home," Irbm, vol. 34, pp. 60-63, 2013. [21] S.-C. Kim, Y.-S. Jeong, and S.-O. Park, "RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments," Personal and ubiquitous computing, vol. 17, pp. 1699-1707, 2013. [22] D.-M. Han and J.-H. Lim, "Smart home energy management system using IEEE 802.15. 4 and zigbee," IEEE Transactions on Consumer Electronics, vol. 56, pp. 1403-1410, 2010. [23] Z. Zou, K.-J. Li, R. Li, and S. Wu, "Smart home system based on ipv6 and zigbee technology," Procedia Engineering, vol. 15, pp. 1529-1533, 2011. [24] Y. Li, K. Ho, and M. Popescu, "A microphone array system for automatic fall detection," IEEE Transactions on Biomedical Engineering, vol. 59, pp. 1291-1301, 2012. [25] G. Koshmak, M. Linden, and A. Loutfi, "Dynamic Bayesian networks for context-aware fall risk assessment," Sensors, vol. 14, pp. 9330-9348, 2014. [26] Y. Xiang, Y.-p. Tang, B.-q. Ma, H.-c. Yan, J. Jiang, and X.-y. Tian, "Remote safety monitoring for elderly persons based on omni-vision analysis," PloS one, vol. 10, p. e0124068, 2015. [27] T.-H. Tsai, A. M.-K. Wong, C.-L. Hsu, and K. C. Tseng, "Research on a community-based platform for promoting health and physical fitness in the elderly community," PLoS One, vol. 8, p. e57452, 2013. [28] N. Suryadevara, A. Gaddam, R. Rayudu, and S. Mukhopadhyay, "Wireless sensors network based safe home to care elderly people: Behaviour detection," Sensors and Actuators A: Physical, vol. 186, pp. 277-283, 2012. [29] J. Choomkasean, P. Mongkolnam, and J. H. Chan, "Multimedia delivery for elderly people: A conceptual model," in International Conference on Advances in Information Technology, 2012, pp. 58-69. [30] P. Spagnoletti, A. Resca, and Ø. Sæbø, "Design for social media engagement: Insights from elderly care assistance," The Journal of Strategic Information Systems, vol. 24, pp. 128-145, 2015. [31] H. Mshali, T. Lemlouma, M. Moloney, and D. Magoni, "A survey on health monitoring systems for health smart homes," International Journal of Industrial Ergonomics, vol. 66, pp. 26-56, 2018. [32] A. Lotfi, C. Langensiepen, S. M. Mahmoud, and M. J. Akhlaghinia, "Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour," Journal of ambient intelligence and humanized computing, vol. 3, pp. 205-218, 2012. [33] J. Wang, N. Spicher, J. M. Warnecke, M. Haghi, J. Schwartze, and T. M. Deserno, "Unobtrusive health monitoring in private spaces: The smart home," Sensors, vol. 21, p. 864, 2021. [34] Z.-y. Liu, "Hardware design of smart home system based on ZigBee wireless sensor network," Aasri Procedia, vol. 8, pp. 75-81, 2014. [35] L. J. Molnar, J. L. Charlton, D. W. Eby, J. Langford, S. Koppel, G. E. Kolenic, et al., "Factors affecting self-regulatory driving practices among older adults," Traffic injury prevention, vol. 15, pp. 262-272, 2014. [36] M. Moraitou, A. Pateli, and S. Fotiou, "Smart health caring home: A systematic review of smart home care for elders and chronic disease patients," GeNeDis 2016, pp. 255-264, 2017. [37] Y. Zhang, G. Tian, and H. Chen, "Exploring the cognitive process for service task in smart home: A robot service mechanism," Future Generation Computer Systems, vol. 102, pp. 588-602, 2020. [38] N. Komninos, E. Philippou, and A. Pitsillides, "Survey in smart grid and smart home security: Issues, challenges and countermeasures," IEEE Communications Surveys & Tutorials, vol. 16, pp. 1933-1954, 2014. [39] J. Dahmen, B. L. Thomas, D. J. Cook, and X. Wang, "Activity learning as a foundation for security monitoring in smart homes," Sensors, vol. 17, p. 737, 2017. [40] Y. Wan, K. Xu, G. Xue, and F. Wang, "IoTArgos: A multi-layer security monitoring system for Internet-of-Things in smart homes," in IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 2020, pp. 874-883. [41] N. Thakur and C. Y. Han, "Framework for an intelligent affect aware smart home environment for elderly people," Int. J. Recent Trends Hum. Comput. Interact.(IJHCI), vol. 9, pp. 23-43, 2019. [42] A. F. Klaib, N. O. Alsrehin, W. Y. Melhem, and H. O. Bashtawi, "IoT smart home using eye tracking and voice interfaces for elderly and special needs people," J. Commun, vol. 14, pp. 614-621, 2019. [43] C. Lohr, P. Tanguy, and Y. Chen, "Plug and play your robot into your smart home: Illustration of a new framework," KI-Künstliche Intelligenz, vol. 31, pp. 283-289, 2017. 1 A01 فاطمه بهرامی، سعید زاهدی فاطمه بهرامی، سعید زاهدی C. فاطمه بهرامی، سعید زاهدی 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2021.0908.0037 https://rcsj.ir/user/articles/152 Farzanegan Pub mEDRA 01 01 152 zagros Publisher IR 01 152 JB 32 1 4 01 202605 7 10 01 [1] Abuaiadah D (2015) Using bisect k-means clustering technique in the analysis of arabic documents. ACM Trans Asian LowResour Lang Inf Process 15(3):1–17 [2] Algergawy A, Mesiti M, Nayak R, Saake G (2011) XML data clustering: an overview. ACM Comput Surv 43(4):1–25 [3] Anderberg MR (2014) Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, vol 19. Academic Press, USA [4] Angiulli F, Fassetti F (2013) Exploiting domain knowledge to detect outliers. Data Min Knowl Discov 28(2):519–568 [5] Angiulli F, Fassetti F (2016) Toward generalizing the unification with statistical outliers: the gradient outlier factor measure. ACM Trans Knowl Discov Data 10(3):1–26 [6] Akulwar, P., Pardeshi,S., and Kamble.A.,(2018), "Survey on Different Data Mining Techniques for Prediction," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2nd International Conference on, Palladam, India, pp. 513-519. [7] Arora RK, Gupta MK (2017) e-Governance using data warehousing and data mining. Int J Comput Appl 169(8):28–31 [8] Bahari, F.T. & Elayidom, M.S. (2015). An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour. Procedia Computer Science. 46. 725-731. 10.1016/j.procs.2015.02.136. [9] Baker RSJ (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds) International encyclopedia of education, 3rd edn. Elsevier, Oxford, UK. [10] Belle.A., Thiagarajan,R.,. Soroushmehr,SMR, Navidi,F., Beard, D. A. and Najarian,K.,(2015), “Big data analytics in healthcare,” BioMed Research International, vol. 2015, Article ID 370194. [11] Bhatnagar V, Ahuja S, Kaur S (2015) Discriminant analysisbased cluster ensemble. Int J Data Min Modell Manag 7(2):83–107 [12] Bochicchio,M. ,Cuzzocrea,A. and Vaira.L.(2016), “A big data analytics framework for supporting multidimensional mining over big healthcare data,” in Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, pp. 508–513, usa [13] Bouguessa M (2013) Clustering categorical data in projected spaces. Data Min Knowl Discov 29(1):3–38 [14] Campello RJGB, Moulavi D, Zimek A, Sander J (2015) Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans Knowl Discov Data 10(1):1–51 [15] Carpineto C, Osinski S, Romano G, Weiss D (2009) A survey of web clustering engines. ACM Comput. Surv. 41(3):1–38 [16] Ceglar A, Roddick JF (2006) Association mining. ACM Comput Surv 38(2):1–42 28. Chen YL, Weng CH (2009) Mining fuzzy association rules from questionnaire data. Knowl Based Syst 22(1):46–56 [17] Chen M, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883 [18] Chan P. K, Salvatore JS (1997) On the accuracy of metalearning for scalable data mining. J Intell Inf Syst 8:5–28 96. [19] Chandra P, Gupta MK (2018) Comprehensive survey on data warehousing research. Int J Inform Technol 10(2):217–224 [20] Chen YL, Weng CH (2009) Mining fuzzy association rules from questionnaire data. Knowl Based Syst 22(1):46–56 [21] Craw S., Wiratunga N., Rowe R. C. (2006) Learning adaptation knowledge to improve case-based reasoning. Artif Intell 170:1175–1192 [22] Data mining examples: most common applications of data mining (2019). https://www.softwaretestinghelp.com/datamining-examples/. Accessed 27 Dec 2019 [23] Data mining—applications & trends. https://www.tutorialspoint. com/data_mining/dm_applications_trends.htm [24] Das R, Kalita J, Bhattacharya (2011) A pattern matching approach for clustering gene expression data. Int J Data Min Model Manag 3(2):130–149 [25] Devi SVSG (2013) Applications and trends in data mining. Orient J Comput Sci Technol 6(4):413–419 [26] Dincer E (2006) The k-means algorithm in data mining and an application in medicine. Kocaeli Univesity, Kocaeli [27] Du.G, Ruhe G.. (2014) Two machine-learning techniques for mining solutions of the ReleasePlannerTM decision support system. Inf Sci 259:474–489 [28] El Aboudi, N. & Benhlima, L. (2018). Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation. Advances in Bioinformatics. 2018. 1-10. 10.1155/2018/4059018. [29] Enke D., Thawornwong S.(2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29:927–940 [30] Esling P, Agon C (2012) Time-series data mining. ACM Comput Surv 45(1):1–34 [31] Fan C.Y., Fan P.Sh., Chan T.Y., Chang Sh.H. (2012) Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Syst Appl 39:8844–8851 [32] Fayadd U, Piatesky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AAAI Press/The MIT Press, Massachusetts Institute of Technology. ISBN 0–262 56097–6 Fayap [33] Fayadd U, Piatesky-Shapiro G, Smyth P (1996) Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the 2nd ACM international conference on knowledge discovery and data mining (KDD), Portland, pp 82–88 [34] Friedman JH (1997) Data mining and statistics: What is the connection? in: Keynote Speech of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, TX, 1997 [35] Fu Y (1997) Data mining: tasks, techniques, and applications. IEEE Potentials 16(4):18–20 [36] Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv 38(3):1–32 [37] Gheware SD, Kejkar AS, Tondare SM (2014) Data mining: tasks, tools, techniques and applications. Int J Adv Res Comput Commun Eng 3(10):8095–8098 [38] Gibert K, Sanchez-Marre M, Codina V (2010) Choosing the right data mining technique: classification of methods and intelligent recommendation. In: International Congress on Environment Modelling and Software Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa, Canada [39] Gupta A, Mumick IS (1995) Maintenance of materialized views: problems, techniques, and applications. IEEE Data Eng Bull 18(2):3 [40] Gupta MK, Chandra P (2019) MP-K-means: modified partition based cluster initialization method for k-means algorithm. Int J Recent Technol Eng 8(4):1140–1148 [41] Gupta MK, Chandra P (2019) HYBCIM: hypercube based cluster initialization method for k-means. IJ Innov Technol Explor Eng 8(10):3584–3587. https://doi.org/10.35940/ijitee. j9774.0881019 [42] Gupta MK, Chandra P (2019) A comparative study of clustering algorithms. In: Proceedings of the 13th INDIACom-2019; IEEE Conference ID: 461816; 6th International Conference on ‘‘Computing for Sustainable Global Development’’ [43] Gupta MK, Chandra P (2019) An efficient approach for selection of initial cluster centroids for k-means clustering algorithm. In: Proceedings international conference on recent developments in science engineering and technology (REDSET-2019), November 15–16 [44] Gupta MK, Chandra P (2019) P-k-means: k-means using partition based cluster initialization method. In: Proceedings of the international conference on advancements in computing and management (ICACM 2019), Elsevier SSRN, pp 567–573 [45] Gupta MK, Chandra P (2019) An empirical evaluation of k-means clustering algorithm using different distance/similarity metrics. In: Proceedings of the international conference on emerging trends in information technology (ICETIT-2019), emerging trends in information technology, LNEE 605 pp 884–892 [46] Gupta, M.K. & Chandra, P. (2020). A comprehensive survey of data mining. International Journal of Information Technology. 1-15. 10.1007/s41870-020-00427-7. [47] Han J, Fu Y (1996) Exploration of the power of attribute-oriented induction in data mining. Adv Knowl Discov Data Min. AAAI/MIT Press, pp 399-421 [48] Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier, Netherlands [49] Hea Z, Xua X, Huangb JZ, Denga S (2004) Mining class outliers: concepts, algorithms and applications in CRM. Expert Syst Appl 27(4):681e97 [50] Heckerman D (1998) A tutorial on learning with Bayesian networks. Learning in graphical models. Springer, Netherlands, pp 301–354 [51] Heikki M (1996) Data mining: machine learning, statistics, and databases. In: SSDBM ’96: proceedings of the eighth international conference on scientific and statistical database management, June 1996, pp 2–9 [52] Hu¨llermeier E. (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156:387–406 [53] Hullermeier E. (2011) Fuzzy sets in machine learning and data mining. Appl Soft Comput 11:1493–1505 [54] Hung LN, Thu TNT, Nguyen GC (2015) An efficient algorithm in mining frequent itemsets with weights over data stream using tree data structure. IJ Intell Syst Appl 12:23–31 [55] Huang Mu-Jung, Tsou Yee-Lin, Lee Show-Chin (2006) Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowl Based Syst 19:396–403 [56] Hung LN, Thu TNT (2016) Mining frequent itemsets with weights over data stream using inverted matrix. IJ Inf Technol Comput Sci 10:63–71 [57] Jackson J (2002) Data mining: a conceptual overview. Commun Assoc Inf Syst 8:267–296 [58] Jain N, Srivastava V (2013) Data mining techniques: a survey paper. Int J Res Eng Technol 2(11):116–119 [59] Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput. Surv 31(3):1–60 [60] Jin H, Wang S, Zhou Q, Li Y (2014) An improved method for density-based clustering. Int J Data Min Model Manag 6(4):347–368 [61] Kanevski M, Parkin R, Pozdnukhov A, Timonin V, Maignan M, Demyanov V, Canu S (2004) Environmental data mining and modelling based on machine learning algorithms and geostatistics. Environ Model Softw 19:845–855. [62] Keles¸ MK (2017) An overview: the impact of data mining applications on various sectors. Tech J 11(3):128–132 [63] Khandare A, Alvi AS (2017) Performance analysis of improved clustering algorithm on real and synthetic data. IJ Comput Netw Inf Secur 10:57–65 [64] Kiranmai B, Damodaram A (2014) A review on evaluation measures for data mining tasks. Int J Eng Comput Sci 3(7):7217–7220 [65] Koh YS, Ravana SD (2016) Unsupervised rare pattern mining: a survey. ACM Trans Knowl Discov Data 10(4):1–29 [66] Kosina P, Gama J (2015) Very fast decision rules for classification in data streams. Data Min Knowl Discov 29(1):168–202 [67] Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268 [68] Kumar D, Bezdek JC, Rajasegarar S, Palaniswami M, Leckie C, Chan J, Gubbi J (2016) Adaptive cluster tendency visualization and anomaly detection for streaming data. ACM Trans Knowl Discov Data 11(2):1–24 [69] Kuzey C, Uyar A, Delen (2014) The impact of multinationality on firm value: a comparative analysis of machine learning techniques. Decis Support Syst 59:127–142 [70] Lee G, Yun U (2017) A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Gener Comput Syst 68:89–110 [71] Lew A, Mauch H (2006) Introduction to data mining and its applications. Springer, Berlin [72] Liao TW, Triantaphyllou E (2007) Recent advances in data mining of enterprise data: algorithms and applications. World Scientific Publishing, Singapore, pp 111–145 [73] Liao SH, Chu PH, Hsiao PY (2012) Data mining techniques and applications—a decade review from 2000 to 2011. Expert Syst Appl 39:11303–11311 [74] Li G, Zaki MJ (2015) Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Discov 30(1):181–225. https://doi.org/10.1007/s10618- 015-0409-y [75] Mabroukeh NR, Ezeife CI (2010) A taxonomy of sequential pattern mining algorithms. ACM Comput Surv 43:1 [76] Maheshwari, A. K.(2015), Business intelligence and data mining. New York, NY : Business Expert Press. [77] Mampaey M, Vreeken J (2011) Summarizing categorical data by clustering attributes. Data Min Knowl Discov 26(1):130–173 [78] Menardi G, Torelli N (2012) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28(1):4–28. https://doi.org/10.1007/s10618-012-0295-5 [79] Mezyk E., Unold O. (2011) Machine learning approach to model sport training. Comput Hum Behav 27:1499–1506 [80] Mihoci A (2017) Modelling limit order book volume covariance structures. In: Hokimoto T (ed) Advances in statistical methodologies and their application to real problems. IntechOpen, Croatia. https://doi.org/10.5772/66152 [81] Moawia,E. Yahia ME, El-taher ME (2010) A new approach for evaluation of data mining techniques. Int J Comput Sci Issues 7(5):181–186 [82] Morik K, Bhaduri K, Kargupta H (2011) Introduction to data mining for sustainability. Data Min Knowl Discov 24(2):311–324 [83] Mukherjee S, Shaw R, Haldar N, Changdar S (2015) A survey of data mining applications and techniques. Int J Comput Sci Inf Technol 6(5):4663–4666 [84] Mukhopadhyay A, Maulik U, Bandyopadhyay S (2015) A survey of multiobjective evolutionary clustering. ACM Comput Surv 47(4):1–46 [85] Olaronke.I., Oluwaseun,O., (2016), Bigatan ealthcare: [86] Prospects, challenges and resolutions,” in Proceedings of the 2016 Future Technologies Conference, FTC 2016, pp. 1152–1157, usa. [87] Padhy N, Mishra P, Panigrahi R (2012) A survey of data mining applications and future scope. Int J Comput Sci Eng Inf Technol 2(3):43–58 [88] Padhraic S (2000) Data mining: analysis on grand scale. Stat Method Med Res 9(4):309–327. https://doi.org/10.1191/ 096228000701555181 [89] Pagels, D. A.(2015), “Aviation Data Mining,” Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, vol. 2(1), Article 3. [90] Pei Y, Fern XZ, Tjahja TV, Rosales R (2016) ‘Comparing clustering with pairwise and relative constraints: a unified framework. ACM Trans Knowl Discov Data 11:2 [91] Politano PM, Walton RO (2017) Statistics & research methodol. Lulu. com [92] Ponniah P (2001) Data warehousing fundamentals. Wiley, USA [93] Rafalak M, Deja M, Wierzbicki A, Nielek R, Kakol M (2016) Web content classification using distributions of subjective quality evaluations. ACM Trans Web 10:4 [94] Raghupathi,W., Raghupathi,V.,(2014), “Big data analytics in healthcare: promise and potential,” Health Information Science and Systems, vol. 2, article 3 [95] Reddy D, Jana PK (2014) A new clustering algorithm based on Voronoi diagram. Int J Data Min Model Manag 6(1):49–64 [96] Rustogi S, Sharma M, Morwal S (2017) Improved Parallel Apriori Algorithm for Multi-cores. IJ Inf Technol Comput Sci 4:18–23 [97] Saranya,K., Premalatha,K., and Rajasekar, S.S.,(2015), "A survey on privacy preserving data mining," 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, pp. 1740-1744, doi: 10.1109/ECS.2015.7124885. [98] Saeed S, Ali M (2012) Privacy-preserving back-propagation and extreme learning machine algorithms. Data Knowl Eng 79–80:40–61 [99] Sawant V, Shah K (2013) A review of distributed data mining using agents. Int J Adv Technol Eng Res 3(5):27–33. [100] Shah-Hosseini H (2013) Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm. Int J Data Min Model Manag 5(4):301–317 [101] Sharma M (2014) Data mining: a literature survey. Int J Emerg Res Manag Technol 3(2):1–4 [102] Silva JA, Faria ER, Barros RC, Hruschka ER, de Carvalho ACPLF, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46(1):1–31 [103] Silva A, Antunes C (2014) Multi-relational pattern mining over data streams. Data Min Knowl Discov 29(6):1783–1814. https:// doi.org/10.1007/s10618-014-0394-6 [104] Singh Y, Bhatia PK, Sangwan OP (2007) A review of studies on machine learning techniques. Int J Comput Sci Secur 1(1):70–84 [105] Sim K, Gopalkrishnan V, Zimek A, Cong G (2012) A survey on enhanced subspace clustering. Data Min Knowl Discov 26(2):332–397 [106] Smith Kate A, Gupta Jatinder ND (2000) Neural networks in business: techniques and applications for the operations researcher. Comput Oper Res 27:1023–1044 [107] Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Hum Behav 68:244–253 [108] Sun,J., Reddy,C.K.(2013), “Big data analytics for healthcare,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 1525, Chicago, Ill, USA [109] Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36(4):8616–8630 [110] Tew C, Giraud-Carrier C, Tanner K, Burton S (2013) Behaviorbased clustering and analysis of interestingness measures for association rule mining. Data Min Knowl Discov 28(4):1004–1045 [111] Thompson B (2004) Exploratory and confirmatory factor analysis: understanding concepts and applications. American Psychological Association, Washington, DC (ISBN:1-59147-093-5) [112] Top 14 useful applications for data mining. https://bigdatamadesimple.com/14-useful-applications-of-data-mining/. Accessed 20 Aug 2014 [113] Tsai Ch.-F., Hsu Y.-F., Lin Ch.-Y., Lin W.-Y.g (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36:11994–12000 [114] Turban E, Aronson JE, Liang TP, Sharda R (2007) Decision support and business intelligence systems. 8th edn, Pearson Education, UK [115] Venkatadri M, Reddy LC (2011) A review on data mining from past to the future. Int J Comput Appl 15(7):19–22 [116] Wang B, Rahal I, Dong A (2011) Parallel hierarchical clustering using weighted confidence affinity. Int J Data Min Model Manag 3(2):110–129 [117] Wang F, Sun J (2014) Survey on distance metric learning and dimensionality reduction in data mining. Data Min Knowl Discov 29:534–564 [118] Wang L, Dong M (2015) Exemplar-based low-rank matrix decomposition for data clustering. Data Min Knowl Discov 29:324–357 [119] Weiss SH, Indurkhya N (1998) Predictive data mining: a practical guide. Morgan Kaufmann Publishers, San Francisco [120] Wetherill GB (1987) Regression analysis with application. Chapman & Hall Ltd, UK [121] Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Making 5(4):597–604 [122] Zacharis NZ (2018) Classification and regression trees (CART) for predictive modeling in blended learning. IJ Intell Syst Appl 3:1–9 [123] Zhang W, Li R, Feng D, Chernikov A, Chrisochoides N, Osgood C, Ji S (2015) Evolutionary soft co-clustering: formulations, algorithms, and applications. Data Min Knowl Discov 29:765–79 1 A01 زهرا نخعی راد زهرا نخعی راد C. زهرا نخعی راد 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2021.0908.0121 https://rcsj.ir/user/articles/153 Farzanegan Pub mEDRA 01 01 153 zagros Publisher IR 01 153 JB 32 1 4 01 202605 7 10 01 [1] KS, D., & Kamath, A. (2017). Survey on Techniques of Data Mining and its Applications. [2] Huang, R. J. P., Depari, G. S., Riorini, S. V., & Wang, P. C. (2018). Leveraging Social Media Metrics in Improving Social Media Performances through Organic Reach: A Data Mining Approach. Review of Economic and Business Studies, 11(2), 33-48.. [3] Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130. [4] Jeffery W. Seifert , Analyst in information science and Technology Policy, ‘ Data [5] Mining : An Overview ‘ December 2004. [6] Sondwale, P. P. (2015). Overview of predictive and descriptive data mining techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 5(4), 262-265. [7] Li, F., Qian, Y., Wang, J., Dang, C., & Jing, L. (2019). Clustering ensemble based on sample's stability. Artificial Intelligence, 273, 37-55. [8] Gupta, M. K., & Chandra, P. (2019, March). A comparative study of clustering algorithms. In 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 801-805). IEEE. [9] Kim, T., Chen, I. R., Lin, Y., Wang, A. Y. Y., Yang, J. Y. H., & Yang, P. (2019). Impact of similarity metrics on single-cell RNA-seq data clustering. Briefings in bioinformatics, 20(6), 2316-2326. [10] Chitra, K., & Maheswari, D. (2017). A comparative study of various clustering algorithms in data mining. International Journal of Computer Science and Mobile Computing, 6(8), 109-115. [11] Pascu, A. I. (2018). DATA MINING. CONCEPTS AND APPLICATIONS IN BANKING SECTOR. Annals of'Constantin Brancusi'University of Targu-Jiu. Economy Series, (1). [12] Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics. [13] Hahsler, M., & Karpienko, R. (2017). Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3), 317-335. [14] Kumar, B. S. (2019). Data Mining: Clustering. Journal of the Gujarat Research Society, 21(14), 2021-2037. [15] Harakawa, R., Takimura, S., Ogawa, T., Haseyama, M., & Iwahashi, M. (2019). Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation. IEEE Access, 7, 116207-116217. [16] KADHIM, M. R., TIAN, W., & KHAN, T. (2019, December). Rapid Clustering with Semi-Supervised Ensemble Density Centers. In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing (pp. 230-235). IEEE. [17] Cai, Z., Yang, X., Huang, T., & Zhu, W. (2020). A new similarity combining reconstruction coefficient with pairwise distance for agglomerative clustering. Information Sciences, 508, 173-182. [18] Gupta, M. K., & Chandra, P. (2019, March). A comparative study of clustering algorithms. In 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 801-805). IEEE. [19] Kim, T., Chen, I. R., Lin, Y., Wang, A. Y. Y., Yang, J. Y. H., & Yang, P. (2019). Impact of similarity metrics on single-cell RNA-seq data clustering. Briefings in bioinformatics, 20(6), 2316-2326. [20] Al Alawi, M., Ray, S., & Gupta, M. (2019). A New Framework for Distance-based Functional Clustering. [21] Gupta, M. K., & Chandra, P. (2020). An Empirical Evaluation of K-Means Clustering Algorithm Using Different Distance/Similarity Metrics. In Proceedings of ICETIT 2019 (pp. 884-892). Springer, Cham. [22] Gupta, M. K., & Chandra, P. HYBCIM: Hypercube Based Cluster Initialization Method for k-means. [23] Huang, T., Wang, S., & Zhu, W. (2020). An adaptive kernelized rank-order distance for clustering non-spherical data with high noise. International Journal of Machine Learning and Cybernetics, 1-13. [24] Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017). A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing, 239, 39-57. [25] Wang, X., Yang, S., Zhao, Y., & Wang, Y. (2018). Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field, Jiyang depression. Journal of Petroleum Science and Engineering, 166, 157-174. Kadhim, A. I. (2019). Survey on supervised machine learning techniques for automatic text classification. Artificial Intelligence Review, 52(1), 273-292 1 A01 محمد حسین زارع، علیرضا دهقانی محمد حسین زارع، علیرضا دهقانی C. محمد حسین زارع، علیرضا دهقانی 01 eng 202605 2026 C. zagros