Subspace and projected clustering: Experimental evaluation and analysis

Knowledge and Information Systems

Volumn 21, Issue 3, 2009, Pages 299-326

  Moise, Gabriela ^a   Zimek, Arthur ^b   Kröger, Peer ^b   Kriegel, Hans Peter ^b   Sander, Jörg ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Projected clustering; Subspace clustering

Indexed keywords

CLUSTER ANALYSIS;

COMPREHENSIVE EVALUATION; EXPERIMENTAL EVALUATION; HIGH DIMENSIONAL DATA; PROJECTED CLUSTERING; STATE OF THE ART; SUB-SPACE CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 71949123741     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-009-0226-y     Document Type: Article

References (44)

1. Achtert E, Böhm C, Kriegel H-P, Kröger P, Müller-Gorman I, Zimek A (2007) Detection and visualization of subspace cluster hierarchies. In: Ramamohanarao K, Krishna P, Mohania M, Nantajeewarawat E (eds) Proceedings of the 12th international conference on database systems for advanced applications (DASFAA), Bangkok, Thailand, 2007, pp 152-163
2. Achtert E, Kriegel H-P, Zimek A (2008) ELKI: a software system for evaluation of subspace clustering algorithms. In: Proceedings of the 20th international conference on scientific and statistical database management (SSDBM), Hong Kong, China
3. Aggarwal C, Hinneburg A, Keim D (2001) On the surprising behavior of distance metrics in high dimensional space. In: Bussche J, Vianu V (eds) Proceedings of the eighth international conference on database theory (ICDT), London, UK, 2001, pp 420-434
4. Aggarwal C, Procopiuc C, Wolf J, Yu P, Park J (1999) Fast algorithms for projected clustering. In: Delis A, Faloutsos C, Ghandeharizadeh, S (eds) Proceedings of the ACM SIGMOD international conference on management of data, Philadelphia, PA, USA, 1999, pp 61-72
5. Aggarwal C, Yu P (2000) Finding generalized projected clusters in high dimensional spaces. In: Chen W, Naughton J, Bernstein P (eds) Proceedings of the ACM SIGMOD international conference on management of data, Dallas, TX, USA, 2000, pp 70-81
6. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high-dimensional data for data mining applications. In: Haas L, Tiwary A (eds) Proceedings of the ACM SIGMOD international conference on management of data, Seattle, WA, USA, 1998, pp 94-105
7. Agrawal R, Srikan R (1994) Fast algorithms for mining association rules in large databases. In: Bocca J, Jarke M, Zaniolo C (eds) Proceedings of the international conference on very large data bases VLDB, Santiago de Chile, Chile, 1994, pp 487-499
8. Ankerst M, Breunig M, Kriegel H-P, Sander J (1999) OPTICS: Ordering points to identify the clustering structure. In: Delis A, Faloutsos C, Ghandeharizadeh S (eds) Proceedings of the ACM international conference on management of data (SIGMOD), Philadelphia, PA, USA, 1999, pp 49-60
9. Assent I, Krieger R, Müller E, Seidl T (2007) DUSC: dimensionality unbiased subspace clustering. In: Proceedings of the seventh international conference on data mining (ICDM), Omaha, NE, USA, 2007, pp 409-414
10. Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is "nearest neighbor" meaningful?. In: Beeri C, Buneman P (eds) Proceedings of the seventh international conference on database theory (ICDT), Jerusalem, Israel, 1999, pp 217-235
11. Böhm C, Kailing K, Kriegel H-P, Kröger P (2004) Density connected clustering with local subspace preferences. In: Proceedings of the fourth international conference on data mining (ICDM), Brighton, UK, 2004, pp 27-34
12. Cheng C, Fu A, Zhang Y (1999) Entropy-based subspace clustering for mining numerical data. In: Proceedings of the fifth ACM international conference on knowledge discovery and data mining (SIGKDD), San Diego, CA, USA, 1999, pp 84-93
13. A Dempster N Laird D Rubin 1977 Maximum likelihood for incomplete data via the EM algorithm J R Stat Soc Ser B 39 1 1 38 0364.62022 501537
14. Ertöz L, Steinbach M, Kumar V (2003) Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Barbara D, Kamath C (eds) Proceedings of the third SIAM international conference on data mining (SDM), San Francisco, CA, USA, 2003
15. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis E, Han J, Fayyad U (eds) Proceedings of the second ACM international conference on knowledge discovery and data mining (KDD), Portland, OR, USA, 1996, pp 226-231
16. D Gondek T Hofmann 2007 Non-redundant data clustering Knowl Inf Syst 12 1 1 116 10.1007/s10115-006-0009-7
17. Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Academic Press, London
18. Hinneburg A, Aggarwal C, Keim D (2000) What is the nearest neighbor in high dimensional spaces? In: Abbadi A, Brodie M, Chakravarthy, Dayal U, Kamel N, Schlageter G, Whang K (eds) Proceedings of the 26th international conference on very large data bases (VLDB), Cairo, Egypt, 2000, pp 506-515
19. A Hinneburg D Keim 2003 A general approach to clustering in large databases with noise Knowl Inf Syst 5 4 387 415 10.1007/s10115-003-0086-9
20. Kailing K, Kriegel H-P, Kröger P (2004) Density-connected subspace clustering for high-dimensional data. In: Berry M, Dayal U, Kamath C, Skilicorn D (eds) Proceedings of the fourth SIAM international conference on data mining (SDM), Orlando, FL, USA, 2004, pp 1-11
21. Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analyis. Wiley, New York
22. Kriegel H-P, Kröger P, Renz M, Wurst S (2005) A generic framework for efficient subspace clustering of high-dimensional data. In: Proceedings of the fifth international conference on data mining, Houston, TX, USA, 2005, pp 250-257
23. Kriegel H-P, Kröger P, Zimek A (2009) Clustering high dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Discov Data 3(1)
24. T Li 2008 Clustering based on matrix approximation: a unifying view Knowl Inf Syst 17 1 1 133 10.1007/s10115-007-0116-0
25. Liu G, Li J, Sim K, Wong L (2007) Distance based subspace clustering with flexible dimension partitioning. In: Proceedings of the 23rd international conference on data engineering (ICDE), Istanbul, Turkey, 2007, pp 1250-1254
26. McQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley symposium on mathematics, statistics, and probabilistics, vol 1, 1967, pp 281-297
27. Moise G, Sander J, Ester M (2006) P3C: A robust projected clustering algorithm. In: Proceedings of the sixth international conference on data mining (ICDM), Hong Kong, China, 2006, pp 414-425
28. G Moise J Sander M Ester 2008 Robust projected clustering Knowl Inf Syst 14 3 273 298 1161.68770 10.1007/s10115-007-0090-6
29. Moise G, Sander J (2008) Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering. In: Proceedings of the 14th ACM international conference on knowledge discovery and data mining (SIGKDD), Las Vegas, NV, USA, 2008, pp 533-541
30. Nagesh H, Goil S, Choudhary A (2001) Adaptive grids for clustering massive data sets. In: Proceedings of the first SIAM international conference on data mining (SDM), Chicago, IL, USA, 2001, pp 1-17
31. Newman D, Hettich S, Blake C, Merz C (1998) UCI repository of machine learning databases 1998
32. K Ng A Fu C Wong 2005 Projective clustering by histograms IEEE Trans Knowl Data Eng 17 3 369 383 10.1109/TKDE.2005.47 (Pubitemid 40320169)
33. Ng R, Han J(1994) Efficient and effective clustering methods for spatial data mining. In: Bocca J, Jarke M, Zaniolo C (eds) Proceedings of the international conference on very large data bases VLDB, Santiago de Chile, Chile, 1994, pp 487-499
34. L Parsons E Haque H Liu 2004 Subspace clustering for high dimensional data: a review ACM SIGKDD Explor Newsl 6 1 90 105 10.1145/1007730.1007731
35. A Patrikainen M Meila 2006 Comparing subspace clusterings IEEE Trans Knowl Data Eng 18 7 902 916 10.1109/TKDE.2006.106 (Pubitemid 44143900)
36. Procopiuc C, Jones M, Agarwal P, Murali T (2002) A Monte Carlo algorithm for fast projective clustering. In: Franklin M, Moon B, Ailamaki, A (eds) Proceedings of the ACM international conference on management of data (SIGMOD, Madison, WI, USA, 2002, pp 418-427
37. R project http://www.r-project.org/
38. UCI Machine Learning Repository http://archive.ics.uci.edu/ml/
39. Sequeira K, Zaki M (2004) SCHISM: a new approach for interesting subspace mining. In: Proceedings of the fourth international conference on data mining (ICDM), Brighton, UK, 2004, pp 186-193
40. J Tang J Chen A Fu W Cheung 2007 Capabilities of outlier detection schemes in large datasets, framework and methodologies Knowl Inf Syst 11 1 45 84 10.1007/s10115-005-0233-6 (Pubitemid 44857542)
41. K Yip D Cheung M Ng 2004 HARP: a practical projected clustering algorithm IEEE Trans Knowl Data Eng 16 11 1387 1397 10.1109/TKDE.2004.74 (Pubitemid 40242327)
42. Yip K, Cheung D, Ng M (2005) On discovery of extremely low-dimensional clusters using semi-supervised projected clustering. In: Proceedings of the 21st international conference on data engineering (ICDE), Tokyo, Japan, 2005, pp 329-340
43. Yip K, Qi P, Schultz M, Cheung D, Cheung K (2006) SemBiosphere: a semantic web approach to recommending microarray clustering services. In: Proceedings of the 11th pacific symposium on biocomputing (PSB), Maui, HI, USA, 2006
44. M Yiu N Mamoulis 2005 Iterative projected clustering by subspace mining IEEE Trans Knowl Data Eng 17 2 176 189 10.1109/TKDE.2005.29 (Pubitemid 40320177)