Locally adaptive metrics for clustering high dimensional data

Data Mining and Knowledge Discovery

Volumn 14, Issue 1, 2007, Pages 63-97

  Domeniconi, Carlotta ^a   Gunopulos, Dimitrios ^b   Ma, Sheng ^c   Yan, Bojun ^a   Al Razgan, Muna ^a   Papadopoulos, Dimitris ^b  

^a GEORGE MASON UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c Shangdi Development Zone   (China)

Author keywords

Clustering ensembles; Dimensionality reduction; Gene expression data; Local feature relevance; Subspace clustering; Text data

Indexed keywords

ADAPTIVE ALGORITHMS; DATA ACQUISITION; DATA REDUCTION; GENETIC ALGORITHMS; INFORMATION ANALYSIS; LINEAR ALGEBRA;

CLUSTERING ENSEMBLES; DIMENSIONALITY REDUCTION; GENE EXPRESSION DATA; LOCAL FEATURE RELEVANCE; SUBSPACE CLUSTERING; TEXT DATA;

DATA STRUCTURES;

EID: 33847338032     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-006-0060-8     Document Type: Article

References (38)

1. Aggarwal C, Procopiuc C, Wolf JL, Yu PS, Park JS (1999) Fast algorithms for projected clustering. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 61-72
2. Aggarwal C, Yu PS (2000) Finding generalized projected clusters in high dimensional spaces. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 70-81
3. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 94-105
4. Alizadeh A et al (2000) Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403(6769):503-511
5. Al-Razgan M, Domeniconi C (2006) Weighted clustering ensembles. In: Proceedings of the SIAM international conference on data mining, pp 258-269
6. Arabie P, Hubert LJ (1996) An overview of combinatorial data analysis. Clustering and classification. World Scientific, Singapore, pp 5-63
7. Bottou L, Vapnik V (1992) Local learning algorithms. Neural Comput 4(6):888-900
8. Chakrabarti K, Mehrotra S (2000) Local dimensionality reduction: a new approach to indexing high dimensional spaces. In: Proceedings of VLDB, pp 89-100
9. Cheng Y, Church GM (2000) Biclustering of expression data. In: Proceedings of the 8th international conference on intelligent systems for molecular biology, pp 93-103
10. Cheeseman P, Stutz J (1996) Bayesian classification (autoclass): theory and results. In: Advances in knowledge discovery and data mining, Chap. 6. AAAI/MIT Press, pp 153-180
11. Dempster AP, Laird NM, Rubin DB (1997) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39(1):l-38
12. Dhillon IS (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 269-274
13. Dhillon IS, Mallela S, Modha DS (2003) Information-theoretic co-clustering. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 89-98
14. Domeniconi C, Papadopoulos D, Gunopulos D, Ma S (2004) Subspace clustering of high dimensional data. In: Proceedings of the SIAM international conference on data mining, pp 517-520
15. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York
16. Dy JG, Brodley CE (2000) Feature subset selection and order identification for unsupervised learning. In: Proceedings of the international conference on machine learning, pp 247-254
17. Ester M, Kriegel HP, Xu X (1995) A database interface for clustering in large spatial databases. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 94-99
18. Fern XZ, Brodley CE (2004) Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of the international conference on machine learning, pp 281-288
19. Friedman J, Meulman J (2002) Clustering objects on subsets of attributes. Technical report, Stanford University
20. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic, New York
21. Ghahramani Z, Hinton GE (1996) The EM algorithm for mixtures of factor analyzers. Technical report CRG-TR-96-1, Department of Computer Science, University of Toronto
22. Hartigan JA (1972) Direct clustering of a data matrix. J Am Stat Assoc 67(337): 123-129
23. Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP, Wilfond B, Borg A, Trent J (2001) Gene expression profiles in hereditary breast cancer. N Engl J Med 344:539-548
24. Keogh E, Chakrabarti K, Mehrotra S, Pazzani M (2001) Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of the ACM SIGMOD conference on management of data, pp 151-162
25. Kharypis G, Kumar V (1995) Multilevel k-way partitioning scheme for irregular graphs. Technical report, Department of Computer Science, University of Minnesota and Army HPC Research Center
26. Michalski RS, Stepp RE (1983) Learning from observation: conceptual clustering. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach, vol 2. Palo Alto TIOGA Publishing Co., pp 331-363
27. Mladenović N, Brimberg J (1996) A degeneracy property in continuous location-allocation problems. In: Les Cahiers du GERAD, G-96-37, Montreal, Canada
28. Modha D, Spangler S (2003) Feature weighting in K-means clustering. Mach Learn 52(3):217-237
29. Ng RT, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: Proceedings of the VLDB conference, pp 144-155
30. Parsons L, Haque E, Liu H (2004) Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor Newsl 6(1):90-105
31. Procopiuc CM, Jones M, Agarwal PK, Murali TM (2002) A Monte Carlo algorithm for fast projective clustering. In: Proceedings of the ACM SIGMOD conference on management of data, pp 418-427
32. Strehl A, Ghosh J (2003) Cluster ensemble - a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583-617
33. Tipping ME, Bishop CM (1999) Mixtures of principal component analyzers. Neural Comput 1(2):443-482
34. Thomasian A, Castelli V, Li CS (1998) Clustering and singular value decomposition for approximate indexing in high dimensional spaces. In: Proceedings of CIKM, pp 201-207
35. Wang H, Wang W, Yang J, Yu PS (2002) Clustering by pattern similarity in large data sets. In: Proceedings of the ACM SIGMOD conference on management of data, pp 394-405
36. Wu CFJ (1983) On the convergence properties of the EM algorithm. Ann Stat 11(1):95-103
37. Yang J, Wang W, Wang H, Yu P (2002) δ-Clusters: capturing subspace correlation in a large data set. In: Proceedings of the international conference on data engineering, pp 517-528
38. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD conference on management of data, pp 103-114