Space decomposition in data mining: A clustering approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2871, Issue , 2003, Pages 24-31

  Rokach, Lior ^a   Maimon, Oded ^a   Lavi, Inbal ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); INTELLIGENT SYSTEMS; K-MEANS CLUSTERING; ALGORITHMS; COMPUTATIONAL COMPLEXITY; DECISION MAKING; PATTERN RECOGNITION; SAMPLING;

CLUSTERING APPROACH; DATA MINING ALGORITHM; DATA SET; DATA SET SIZE; DECOMPOSITION METHODS; ERROR RATE; LARGE AMOUNTS; SPACE DECOMPOSITION;

DATA MINING;

DATA SETS; DIVIDE-AND-INDUCE METHODS; HOMOGENEITY;

EID: 7444228672     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39592-8_5     Document Type: Conference Paper

References (13)

1. Bauer, E. and Kohavi, R. An empirical comparison of voting classification algorithms: bagging, boosting and variants. Machine learning, 36, 105-142, 1999.
2. Dhillon, I. S. and Modha, D. S. A data clustering algorithm on distributed memory machines. In M. Zaki and C.-T. Ho, editors, Lecture Notes in Artificial Intelligence, 1759: pp 245-260, 2000.
3. Fayyad, U., Piatesky-Shapiro, G., and Smyth P., From Data Mining to Knowledge Discovery: An Overview, in U.Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pp 1-30, MIT Press, 1996.
4. Hartigan, J. A. Clustering algorithms. John Wiley & Sons., 1975.
5. Huang, Z. Clustering large data sets with mixed numeric and categorical values. In proceedings of the first Pacific-Asia conference on knowledge discovery and data mining, Singapore, 1997.
6. Kim, D.J., Park, Y.W. and Park ,. A novel validity index for determination of the optimal number of clusters. IEICE Trans. Inf., Vol. E84-D, no.2 (2001), 281-285.
7. Kusiak, A. Decomposition in data mining: An industrial case study, IEEE Trans. Electron. Packag. Manufact., vol. 23, pp. 345-353, Oct. 2000.
8. Maimon, O. and Rokach L. Theory and Applications of Attribute Decomposition. In IEEE International Conference on Data Mining, 473-480, 2001.
9. Provost, F.J. and Kolluri, V. A Survey of Methods for Scaling Up Inductive Learning Algorithms. Proc. 3rd International Conference on Knowledge Discovery and Data Mining, 1997.
10. Ray, S., and Turi, R.H. Determination of Number of Clusters in K-Means Clustering and Application in Color Image Segmentation. Monash university, 1999.
11. Tibshirani, R., Walther, G. and Hastie, T. (2000). Estimating the number of clusters in a dataset via the gap statistic. Tech. Rep. 208, Dept. of Statistics, Stanford University.
12. Wang, X. and Yu, Q. Estimate the number of clusters in web documents via gap statistic. May 2001.
13. Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10 (7).