HE-Tree: A framework for detecting changes in clustering structure for categorical data streams

VLDB Journal

Volumn 18, Issue 6, 2009, Pages 1241-1260

  Chen, Keke ^a   Liu, Ling ^b  

^a Wright State University   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

Categorical data clustering; Change detection; Data stream mining

Indexed keywords

CATEGORICAL DATA; CATEGORICAL DATA CLUSTERING; CHANGE DETECTION; CLUSTERING INFORMATION; CRITICAL INFORMATION; DATA STREAM; DATA STREAM MINING; ENTROPY PROPERTIES; HIGH QUALITY; NUMBER OF CLUSTERS; NUMERICAL DATA; REAL-TIME DECISION MAKING; TREE STRUCTURES;

CLUSTERING ALGORITHMS; DATA COMMUNICATION SYSTEMS; ENTROPY; HYDRAULICS; SIGNAL DETECTION;

CLUSTER ANALYSIS;

EID: 71349088018     PISSN: 10668888     EISSN: 0949877X     Source Type: Journal    
DOI: 10.1007/s00778-009-0134-5     Document Type: Article

References (40)

1. C.C. Aggarwal 2005 On change diagnosis in evolving data streams IEEE Trans. Knowl. Data Eng. 17 5 10.1109/TKDE.2005.78 (Pubitemid 40750819)
2. Aggarwal, C.C., Han, J., Wang, J., Yu, P.: A framework for projected clustering of high dimensional data streams. In: Proceedings of Very Large Databases Conference (VLDB) (2004)
3. Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik, K.C.: Limbo: scalable clustering of categorical data. In: Proceedings of Intternational Conference on Extending Database Technology (EDBT) (2004)
4. Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of ACM Conference on Principles of Database Systems (PODS) (2002)
5. Barbara, D., Li, Y., Couto, J.: Coolcat: an entropy-based algorithm for categorical clustering. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM) (2002)
6. Ben-David, S., Gehrke, J., Kifer, D.: Detecting change in data stream. In: Proceedings of Very Large Databases Conference (VLDB) (2004)
7. Bock, H.: Probabilistic aspects in cluster analysis. In: Conceptual and Numerical Analysis of Data. Springer, Berlin (1989)
8. Brand, M.: An entropic estimator for structure discovery. In: Proceedings Of Neural Information Processing Systems (NIPS), pp. 723-729 (1998)
9. Celeux, G., Govaert, G.: Clustering criteria for discrete data and latent class models. J. Classif. (1991)
10. Chakrabarti, A., Cormode, G., McGregor, A.: A near-optimal algorithm for computing the entropy of a stream. In: Annual ACM-SIAM Symposium on Discrete Algorithms (2007)
11. Chakrabarti, D., Papadimitriou, S., Modha, D.S., Faloutsos, C.: Fully automatic cross-associations. In: Proceedings of ACM SIGKDD Conference (2004)
12. K. Chen L. Liu 2004 VISTA: Validating and refining clusters via visualization Inf. Vis. 3 4 257 270 10.1057/palgrave.ivs.9500076
13. Chen, K., Liu, L.: The "best k" for entropy-based categorical clustering. In: Proceedings of International Conference on Scientific and Statistical Database Management (SSDBM), pp.253-262 (2005)
14. Chen, K., Liu, L.: Detecting the change of clustering structure in categorical data streams. In: SIAM Data Mining Conference (2006)
15. Chen, K., Liu, L.: iVIBRATE: Interactive visualization based framework for clustering large datasets. ACM Trans. Inf. Syst. 24, 2 (2006)
16. Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multidimensional regression analysis of time-series data streams. In : Proceedings of Very Large Databases Conference (VLDB) (2002)
17. Cheng, C.H., Fu, A. W.-C., Zhang, Y.: Entropy-based subspace clustering for mining numerical data. In: Proceedings of ACM SIGKDD Conference (1999)
18. Cover T., Thomas J.: Elements of Information Theory. Wiley, NY (1991)
19. Das, A., Gehrke, J., Riedewald, M.: Approximate join processing over data streams. In: Proceedings of ACM SIGMOD Conference (2003)
20. Dhillon, I.S., Mellela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings of ACM SIGKDD Conference (2003)
21. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of ACM SIGKDD Conference, pp. 71-80 (2000)
22. Ganti, V., Gehrke, J., Ramakrishnan, R.: CACTUS-clustering categorical data using summaries. In: Proceedings of ACM SIGKDD Conference (1999)
23. V. Ganti J. Gehrke R. Ramakrishnan 2001 Demon: Mining and monitoring evolving data IEEE Trans. Knowl. Data Eng. 13 1 10.1109/69.908980 (Pubitemid 32301043)
24. V. Ganti J. Gehrke R. Ramakrishnan W. Loh 2002 A framework for measuring differences in data characteristics J. Comput. Syst. Sci. 64 3 1915025
25. Gehrke, J., Ganti, V., Ramakrishnan, R., Loh, W.-Y.: BOAT- optimistic decision tree construction. In: Proceedings of ACM SIGMOD Conference, pp. 169-180 (1999)
26. Gibson, D., Kleinberg, J., Raghavan, P.: Clustering categorical data: An approach based on dynamical systems. In: Proceedings of Very Large Databases Conference (VLDB), pp. 222-236 (2000)
27. Guha, S., Meyerson, A., Mishra, N., Motwani, R., O'Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. Knowl. Data Eng. 15 (2003)
28. S. Guha R. Rastogi K. Shim 2000 ROCK: A robust clustering algorithm for categorical attributes Inf. Syst. 25 5 345 366 10.1016/S0306-4379(00)00022-3 (Pubitemid 30901386)
29. M. Halkidi Y. Batistakis M. Vazirgiannis 2002 Cluster validity methods: Part I and II SIGMOD Record 31 2 40 45 10.1145/565117.565124
30. Huang, Z.: A fast clustering algorithm to cluster very large categorical data sets in data mining. In: Workshop on Research Issues on Data Mining and Knowledge Discovery (1997)
31. A. Jain M. Murty Flynn P. 1999 Data clustering: A review ACM Comput. Surv. 31 264 323 10.1145/331499.331504
32. Jain A.K., Dubes R.C.: Algorithms for Clustering Data. Prentice hall, New York (1988)
33. Keogh, E., Lin, J., Truppel, W.: Clustering of time series subsequences is meaningless: Implications for previous and future research. In: Proceedings of International Conference on Data Mining (ICDM) (2003)
34. Li, T., Ma, S., Ogihara, M.: Entropy-based criterion in categorical clustering. In: Proceedings of International Conference on Machine Learning (ICML) (2004)
35. C. Meek B. Thiesson D. Heckerman 2002 The learning-curve sampling method applied to model-based clustering J. Mach. Learn. Res. 2 397 418 1007.68082 10.1162/153244302760200678 1929414 (Pubitemid 135712567)
36. Singh, S., Mayfield, C., Prabhakar, S., Shah, R., Hambrusch, S.: Indexing uncertain categorical data. In: Proceedings of IEEE International Conference on Data Engineering (ICDE) (2007)
37. Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method. In: Proceedingd of the 37-th Annual Allerton Conference on Communication, Control and Computing (1999)
38. P.E. Utgoff 1989 Incremental induction of decision trees Mach. Learn. 4 161 186 10.1023/A:1022699900025
39. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept drifting data streams using ensemble classifiers. In: Proceedings of ACM SIGKDD Conference (2003)
40. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Fisher, D.H. (ed.) Proceedings of ICML-97, 14th International Conference on Machine Learning (Nashville, US, 1997), pp. 412-420. Morgan Kaufmann, San Francisco (1997)