DHCC: Divisive hierarchical clustering of categorical data

Data Mining and Knowledge Discovery

Volumn 24, Issue 1, 2012, Pages 103-135

  Xiong, Tengke ^a   Wang, Shengrui ^a   Mayers, André ^a   Monga, Ernest ^a  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

Categorical data; Divisive hierarchical clustering; Subspace clustering

Indexed keywords

CATEGORICAL DATA; DATA REPRESENTATIONS; DENDROGRAMS; DISSIMILARITY MEASURES; DIVISIVE HIERARCHICAL CLUSTERING; LARGE DATASETS; MULTIPLE CORRESPONDENCE ANALYSIS; NUMBER OF CLUSTERS; SIMILARITY MEASURE; SPLITTING PROCESS; SUBSPACE CLUSTERING; SYNTHETIC AND REAL DATA;

COMPUTER APPLICATIONS; DATA MINING;

CLUSTERING ALGORITHMS;

EID: 84856625019     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-011-0221-2     Document Type: Article

References (40)

1. Abdi H, Valentin D (2007) Multiple correspondence analysis. In: Saltkind N (eds) Encyclopedia of measurement and statistics. Sage, Thousand Oaks
2. Aggarwal CC, Wolf JL, Yu PS, Procopiuc C, Park JS (1999) Fast algorithms for projected clustering. In: Proceedings of the 1999 ACM SIGMOD international conference on management of data, Philadelphia, PA, USA, pp 61-72
3. Andritsos P, Tsaparas P, Miller R, Sevcik K (2004) LIMBO: scalable clustering of categorical data. Lecture notes in computer science. Springer, New York
4. Barbara D, Li Y, Couto J (2002) COOLCAT: an entropy-based algorithm for categorical clustering. In: Proceedings of the 11th ACM international conference on information and knowledge management, McLean, VA, USA, pp 582-589
5. M Bouguessa S Wang 2009 Mining projected clusters in high-dimensional spaces IEEE Trans Knowl Data Eng 21 507 522 10.1109/TKDE.2008.162
6. M Brand 2006 Fast low-rank modifications of the thin singular value decomposition Linear Algebra Appl 415 20 30 10.1016/j.laa.2005.07.021 1088.65037 2214744
7. E Cesario G Manco R Ortale 2007 Top-down parameter-free clustering of high-dimensional categorical data IEEE Trans Knowl Data Eng 19 1607 1624 10.1109/TKDE.2007.190649
8. Chen K, Liu L (2005) The 'best k' for entropy-based categorical data clustering. In: Proceedings of the 17th international conference on scientific and statistical database management, Santa Barbara, CA, USA, pp 253-262
9. HL Chen KT Chuang MS Chen 2008 On data labeling for clustering categorical data IEEE Trans Knowl Data Eng 20 1458 1472 10.1109/TKDE.2008.81
10. Das K, Schneider J (2007) Detecting anomalous records in categorical datasets. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose, CA, USA, pp 220-229
11. Ding C, He X (2002) Cluster merging and splitting in hierarchical clustering algorithms. In: Proceedings of the 2nd IEEE international conference on data mining, Maebashi, Japan, pp 139-146
12. Do H, Kim J (2008) Categorical data clustering using the combinations of attribute values. Lecture notes in computer science. Springer, New York
13. Drineas P, Drinea E, Huggins P (2003) An experimental evaluation of a Monte-Carlo algorithm for singular value decomposition. Lecture notes in computer science. Springer, New York
14. Everitt B, Landau S, Leese M (2001) Cluster analysis 4. Arnold Publishers, London
15. G Gan J Wu 2004 Subspace clustering for high dimensional categorical data SIGKDD Explor 6 2 87 94 10.1145/1046456.1046468
16. Ganti V, Gehrke J, Ramakrishnan R (1999) CACTUS: clustering categorical data using summaries. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA, pp 73-83
17. J Grabmeier A Rudolph 2002 Techniques of cluster algorithms in data mining Data Min Knowl Discov 6 4 303 360 10.1023/A:1016308404627 1917947
18. Greenacre M (1993) Correspondence analysis in practice. Academic Press, London
19. Greenacre M, Blasius J (2006) Multiple correspondence analysis and related methods. Chapman & Hall, London
20. Guha S, Rastogi R, Shim K (1999) ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of the 15th IEEE international conference on data engineering, Sydney, Australia, pp 512-521
21. Z Huang 1998 Extensions to the k-means algorithm for clustering large data sets with categorical value Data Mining Knowl Discov 2 283 304 10.1023/A:1009769707641
22. JZ Huang MK Ng H Rong Z Li 2005 Automated variable weighting in k-means type clustering IEEE Trans Pattern Anal Mach Intell 27 5 657 668 10.1109/TPAMI.2005.95
23. A Jain M Murty P Flynn 1999 Data clustering: a review ACM Comput Surv 31 3 264 323 10.1145/331499.331504
24. R Jin Y Breitbart C Muoh 2009 Data discretization unification Knowl Inf Syst 19 1 29 10.1007/s10115-008-0142-6
25. Keogh E, Lonardi S, Ratanamahatana C (2004) Towards parameter-free data mining. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, USA, pp 206-215
26. Li T, Ma S, Ogihara M (2004) Entropy-based criterion in categorical clustering. In: Proceedings of the 21st international conference on machine learning, Banff, Alberta, Canada, pp 536-543
27. Y Lu S Wang S Li C Zhou 2011 Particle swarm optimizer for variable weighting in clustering high-dimensional data Mach Learn 82 43 70 10.1007/s10994-009-5154-2
28. Messaoud R, Boussaid O, Rabaseda S (2006) Efficient multidimensional data representations based on multiple correspondence analysis. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, Philadelphia, PA, USA, pp 662-667
29. 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
30. Salvador S, Chan P (2004) Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. In: Proceedings of the 16th IEEE international conference on tools with artificial intelligence, Boca Raton, FL, USA, pp 576-584
31. O San V Huynh Y Nakamori 2004 An alternative extension of the k-means algorithm for clustering categorical data Int J Appl Math Comput Sci 14 2 241 247 1061.62091 2068056
32. H Sun S Wang Q Jiang 2004 FCM-based model selection algorithm for determining the number of clusters Pattern Recognit 37 10 2027 2037 10.1016/j.patcog.2004.03.012 1056.68583
33. Tan P, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley, Reading
34. Wang K, Xu C, Liu B (1999) Clustering transactions using large items. In: Proceeding of the 10th ACM conference on information and knowledge management (CIKM), Kansas City, MO, USA, pp 483-490
35. Wu J, Xiong H, Chen J (2009) Adapting the right measures for k-means clustering. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, Paris, France, pp 877-885
36. Xiong T, Wang S, Mayers A, Monga E (2008) Personal bankruptcy prediction using sequence mining. In: Proceeding of KDD2008 workshop on data mining for business applications, Las Vegas, NV, USA, pp 32-38
37. Xiong T, Wang S, Mayers A, Monga E (2009) A new MCA-based divisive hierarchical algorithm for clustering categorical data. In: Proceedings of the 9th IEEE international conference on data mining, Miami, FL, USA, pp 1058-1063
38. Yan H, Chen K, Liu L (2006) Efficiently clustering transactional data with weighted coverage density. In: Proceeding of the 17th ACM conference on information and knowledge management (CIKM), Arlington, Virginia, USA, pp 367-376
39. Yang Y, Guan X, You J (2002) CLOPE: a fast and effective clustering algorithm for transactional data. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton, Alberta, Canada, pp 682-687
40. Y Zhao G Karypis 2005 Hierarchical clustering algorithms for document datasets Data Mining Knowl Discov 10 141 168 10.1007/s10618-005-0361-3 2138240