-
آرشیو :
نسخه بهار 1400
-
نوع مقاله :
پژوهشی
-
شناسه دیجیتال (DOI) :
10.22034/RCSJ.2021.0622.0932
-
کد پذیرش :
1378
-
موضوع :
سایر شاخه های علوم رایانه
-
نویسنده/گان :
رقیه مجد آبادی، علیرضا دهقانی
-
کلید واژه :
داده کاوی، خوشه بندی اجماعی، خوشه بندی وزن دار.
-
مراجع :
[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.
- صفحات : 1-13
-
دانلود فایل
( 436.28 KB )