Integrated constraint based clustering algorithm for high dimensional data

Neurocomputing

Volumn 142, Issue , 2014, Pages 478-485

  Liu, Xinyue ^a   Li, Menggang ^b  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Constraint based clustering; High dimensional data; Subspace clustering

Indexed keywords

ITERATIVE METHODS;

CONSTRAINT-BASED CLUSTERING; DATA ASSIGNMENTS; DIMENSION SELECTION; HIGH DIMENSIONAL DATA; OPTIMIZATION TECHNIQUES; SUB-SPACE CLUSTERING; SYNTHETIC DATASETS; UNSUPERVISED ALGORITHMS;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; CDCDD ALGORITHM; CLUSTER ANALYSIS; CLWC ALGORITHM; CONTROLLED STUDY; DATA PROCESSING; DIMENSIONAL MEASUREMENT ACCURACY; FINDIT ALGORITHM; HIGH DIMENSIONAL DATA CLUSTERING; INTEGRATED CONSTRAINT BASED CLUSTERING ALGORITHM; INTERMETHOD COMPARISON; LAC ALGORITHM; MEASUREMENT; MEASUREMENT ACCURACY; MEASUREMENT EFFICIENCY; MEASUREMENT SCALABILITY; PRIORITY JOURNAL; SC CL ALGORITHM; SC MINIER ALGORITHM;

EID: 84904368528     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.04.013     Document Type: Article

References (25)

1. C. Aggarwal, C. Procopiuc, J. Wolf, et al., Fast algorithms for projected clustering, in: Proceedings of the ACM SIGMOD'99, 1999, pp. 61-72.
2. C. Aggarwal, P. Yu, Finding generalized projected clusters in high dimensional space, in: Proceedings of the SIGMOD'00, 2000.
3. R. Agrawal, J. Gehrke, D. Gunopulos, et al., Automatic subspace clustering of high dimensional data for data mining applications, in: Proceedings of the ACM SIGMOD'98, 1998, pp. 94-105.
4. I. Assent, M. Krieger, E. Muller, et al., DUSC: dimensionality unbiased subspace clustering, in: Proceedings of the ICDM'07, 2007.
5. A. Asuncion, D. Newman, UCI Machine Learning Repository, 2007,http://archive.ics.uci.edu/ml/.
6. K. Bache, M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA, 2013, http://archive.ics.uci.edu/ml/.
7. Bar-Hillel A., Hertz T., Shental N., et al. Learning a mahalanobis metric from equivalence constraints. J. Mach. Learn. Res. 2005, 6:937-965.
8. S. Basu, I. Dadidson, K. Wagstaff, Constrained Clustering: Advances in Algorithms, Theory, and Applications, Chapman and Hall/CRC, 2008.
9. C. Bohm, K. Kailing, H.-P. Kriegel, P. Kroger, Densigy connected clustering with local subspace preferences, in: Proceedings of the ICDM'04, 2004.
10. H. Cevilkalp, J. Verbeek, F. Jurie, et al., Semi-supervised dimensionality reduction using pairwise equivalence constraints, in: Proceedings of the CVTA'08, 2008.
11. C. Cheng, A. Fu, Y. Zhang, Entropy-Based subspace clustering for mining numerical data, in: Proceedings of the SIGKDD'99, 1999, pp. 84-93.
12. H. Cheng, K.A. Hua, K. Vu, Constrained locally weighted clustering, in: Proceedings of the VLDB'04, 2008, pp. 90-101.
13. C. Domeniconi, D. Papadopoulos, D. Gunopulos, S. Ma, Subspace clustering of high dimensional data, in: Proceedings of the SDM'04, 2004.
14. Friedman J.H., Meulman J.J. Clustering objects on subsets of attributes (with discussion). J. R. Stat. Soc.: Ser. B Stat. Methodol. 2004, 4:815-849.
15. E. Fromont, A. Prado, C. Robardet, 2009. Constraint based subspace clustering, in: Proceedings of the SDM'09, 2009.
16. H. Kriegel, P. Kroger, A. Zimek, Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering, ACM Trans. Knowl. Disc. Data, 3 (2009), Article 1.
17. E. Müller, S. Günnemann, I. Assent, T. Seidl, Evaluating clustering in subspace projections of high dimensional data, in: Proceedings of the VLDB,09, 2009, pp. 1270-1281.
18. H. Nagesh, S. Goil, A. Choudhary, Adaptive grids for clustering massive data sets, in: Proceedings of the SDM'01, 2001.
19. Jain A.K. Data clustering. 50 years beyond K-means. Pattern Recognit. Lett. 2010, 31:651-666.
20. Parsons L., Haque E., Liu H. Subspace clustering for high dimensional data. a review. SIGKDD Explor. 2010, 90-105.
21. Woo K., Lee J., Kim M. FINDIT. a fast and intelligent subspace clustering algorithm using dimension voting. Inf. Softw. Technol. 2004, 46:255-271.
22. Xu L. BYY harmony learning, structural RPCL, and topological self-organizing on mixture models. Neural Netw. 2002, 15:1125-1151.
23. L. Xu, A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving, Pattern Recognit. 40 (2007) 2129-2153.
24. Zeng H., Cheung Y-M. Feature selection and kernel learning for local learning-based clustering. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(8):1532-2547.
25. X.C. Zhang, Y. Wu, Q. Qiu, Constraint based dimension correlation and distance divergence for clustering high-dimensional data, in: Proceedings of the ICDM'10, 2010.