Learning from Cluster Examples

Machine Learning

Volumn 53, Issue 3, 2003, Pages 199-233

  Kamishima, Toshihiro ^a   Motoyoshi, Fumio ^a  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

Clustering; Dot pattern; Image segmentation; Learning from examples

Indexed keywords

ALGORITHMS; DATA REDUCTION; IMAGE SEGMENTATION; VECTORS;

CLUSTERING; DOT PATTERNS;

LEARNING SYSTEMS;

EID: 0345306654     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1026351106797     Document Type: Article

References (26)

1. Bensaid, A. M., Hall, L. O., Bezdek, J. C., & Clarke, L. P. (1996). Partially supervised clustering for image segmentation. Pattern Recognition, 29:5, 859-871.
2. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth Inc.
3. Burset, M., & Guigó, R. (1996). Evaluation of gene structure prediction programs. Genomics, 34, 353-367.
4. Cheeseman, P., & Stutz, J. (1996). Bayesian classification (AutoClass): Theory and results. In U. M. Fayyad, G. Diatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (pp. 153-180). AAAI Press/The MIT Press, Chapt. 6.
5. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society (B), 39:1, 1-38.
6. Emde, W. (1994). Inductive learning of characteristic concept descriptions from small sets of classified examples. In Proc. of European Conference ofMacine Learning, (pp. 103-121). [LNAI 784].
7. Everitt, B. S. (1993). Cluster Analysis. Edward Arnold, 3rd ed.
8. Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139-172.
9. Itoh, S. (1992). Application of MDL principle to pattern classification problems. J. of Japanese Society for Artificial Intelligence, 7:4. 608-614 (in Japanese).
10. Jain, A. K., & Dubes, R. C. (1988). Algorithms for Clustering Data. Prentice Hall.
11. Kamishima, T., Minoh, M., & Ikeda, K. (1995). Rule formulation based on inductive learning for extraction and classification of diagram symbols. Transactions of The Information Processing Society of Japan, 36:3, 614-626 (in Japanese).
12. McCallum, A., Nigam, K., & Ungar, L. H. (2000). Efficient clustering of high-dimensional data sets with application to reference matching. In Proc. of ACM SIGKDD (pp. 169-178).
13. Michalski, R. S. (1993). Inferential theory of learning as a conceptual basis for multistrategy learning. Machine Learning, 11, 111-151.
14. Pagallo, G., & Haussler, D. (1990). Boolean feature discovery in empirical learning. Machine Learning, 5, 71-99.
15. Pavlidis, T. (1992). Why progress in machine vision is so slow. Pattern Recognition Letters, 13, 221-225.
16. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
17. Quinlan, J. R., & Rivest, R. L. (1989). Inferring decision trees using the minimum description length principle. Information and Computation, 80, 227-248.
18. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. J. of the American Statistical Association, 66, 846-850.
19. Rissanen, J. (1983). A universal prior for integers and estimation by minimum description length. The Annals of Statistics, 11:2, 416-431.
20. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.
21. Urquhart, R. (1982). Graph theoretical clustering based on limited neghbourhood sets. Pattern Recognition, 15:3, 173-187.
22. Wallace, C. S., & Patrick, J. D. (1993). Coding decision trees. Machine Learning, 11, 7-22.
23. Wong, M. A., & Lane, T. (1983). A kth nearest neighbour clustering procedure. Journal of the Royal Statistical Society (B). 45:3, 362-368.
24. Yamanishi, K. (1992). A learning criterion for stochastic rules. Machine Learning, 9, 165-203.
25. Yamanishi, K., & Han, T. (1992). Introduction to MDL from viewpoints of information theory. J. of Japanese Society for Artificial Intelligence, 7:3, 427-434 (in Japanese).
26. Zahn, C. T. (1971). Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. on Computers, 20:1, 68-86.