A robust seedless algorithm for correlation clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6118 LNAI, Issue PART 1, 2010, Pages 28-37

  Aziz, Mohammad S ^a   Reddy, Chandan K ^a  

^a Wayne State University   (United States)

Author keywords

Correlation clustering; Principal component analysis

Indexed keywords

CORRELATION CLUSTER; CORRELATION CLUSTERING; HIGH DIMENSIONAL DATA; NOISE LEVELS; OPTIMAL RESULTS; PRINCIPAL COMPONENTS; REAL-WORLD DATASETS; RESEARCH PROBLEMS;

CLUSTERING ALGORITHMS; CORRELATION METHODS; DATA MINING; PRINCIPAL COMPONENT ANALYSIS;

CLUSTER ANALYSIS;

EID: 79956313271     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13657-3_6     Document Type: Conference Paper

References (13)

1. Achtert, E., Böhm, C., David, J., Kröger, P., Zimek, A.: Robust clustering in arbitrarily oriented subspaces. In: Proceedings of SIAM International Conference on Data Mining (SDM), pp. 763-774 (2008)
2. Aggarwal, C., Yu, P.: Finding generalized projected clusters in high dimensional spaces. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 70-81 (2000)
3. Aggarwal, C.C., Wolf, J.L., Yu, P.S., Procopiuc, C., Park, J.S.: Fast algorithms for projected clustering. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 61-72 (1999) (Pubitemid 129597324)
4. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 94-105 (1998) (Pubitemid 128655960)
5. Bohm, C., Kailing, K., Kroger, P., Zimek, A.: Computing clusters of correlation connected objects. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 455-466 (2004)
6. Cheng, C., Fu, A.W., Zhang, Y.: ENCLUS: Entropy-based subspace clustering for mining numerical data. In: Proceedings of the ACM conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 84-93 (1999)
7. Ding, C.H.Q., He, X., Zha, H., Simon, H.D.: Adaptive dimension reduction for clustering high dimensional data. In: Proceedings of the IEEE International Conference on Data Mining, pp. 147-154 (2002)
8. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the ACM conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 226-231 (1996)
9. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
10. Kailing, K., Kriegel, H., Kröger, P.: Density-connected subspace clustering for highdimensional data. In: Proceedings of SIAM International Conference on Data Mining (SDM), pp. 246-257 (2004)
11. Kriegel, H., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACMTransactions on Knowledge Discovery from Data (TKDD) 3(1), 1-58 (2009)
12. Yip, K.Y., Cheung, D.W., Ng, M.K.: On discovery of extremely low-dimensional clusters using semi-supervised projected clustering. In: Proceedings of the 21st International Conference on Data Engineering (ICDE), pp. 329-340 (2005) (Pubitemid 41731147)
13. Yip, K.Y., Ng, M.K.: Harp: A practical projected clustering algorithm. IEEE Transactions on Knowledge and Data Engeneering (TKDE) 16(11), 1387-1397 (2004) (Pubitemid 40242327)