Feature weighting on k-means clustering

Machine Learning

Volumn 52, Issue 3, 2003, Pages 217-237

  Modha, Dharmendra S ^a   Spangler, W Scott ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

Author keywords

Clustering; Convex k means algorithm; Convexity; Feature combination; Feature selection; Fisher's discriminant analysis; Text mining; Unsupervised learning

Indexed keywords

ALGORITHMS; DATA MINING; DATA STRUCTURES; FEATURE EXTRACTION; OPTIMIZATION; TEXT PROCESSING; VECTOR QUANTIZATION;

CONVEX K-MEANS ALGORITHM; DATA OBJECT; DATA SETS; FEATURE WEIGHTING; FISHER DISCRIMINANT ANALYSIS; UNSUPERVISED LEARNING;

LEARNING SYSTEMS;

EID: 0042312608     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1024016609528     Document Type: Article

References (34)

1. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Proc. Int. Conf. Data Eng. (pp. 3-14).
2. Ahonen-Myka, H. (1999). Finding all maximal frequent sequences in text. In D. Mladenic & M. Grobelnik (eds.), ICML-99 Workshop: Machine Learning in Text Data Analysis (pp. 11-17).
3. Bay, S.D. (1999). The UCI KDD archive. Dept. Inform. and Comput. Sci., Univ. California, Irvine, CA. Available at http://kdd.ics.uci.edu.
4. Blake, C., & Merz, C. (1998). UCI repository of machine learning databases. Dept. Inform. and Comput. Sci., Univ. California, Irvine, CA. Available at http://www.ics.uci.edu/-mlearn/MLRepository.html.
5. Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97, 245-271.
6. Bradley, P., & Fayyad, U. (1998). Refining initial points for k-means clustering. In Proc. 16th Int. Machine Learning Conf., (pp. 91-99), Bled, Slovenia.
7. Caruana, R., & Freitag, D. (1994). Greedy attribute selection. In Proc. 11th Int. Machine Learning Conf. (pp. 28-36).
8. Devaney, M., & Ram, A. (1997). Efficient feature selection in conceptual clustering, In Proc. 14th Int. Machine Learning Conf. (pp. 92-97), Nashville, TN.
9. Dhillon, I. S., & Modha, D. S. (2001). Concept decompositions for large sparse text data using clustering. Machine Learning, 42:1/2, 143-175.
10. Duda, R. O., & Hart, P. E. (1973). Pattern Classification and Scene Analysis. Wiley.
11. Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 139-172.
12. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., & Yanker, P. (1995). Query by image and video content: The QBIC system. IEEE Computer, 28:9, 23-32.
13. Frakes, W. B., & Baeza-Yates, R. (1992). Information Retrieval: Data Structures and Algorithms. New Jersey: Prentice Hall, Englewood Cliffs.
14. Hartigan, J. A. (1975). Clustering Algorithms, Wiley.
15. Joachims, T. (1997). A probabilistic analysis of the Rocchio Algorithm with TFIDF for text categorization. In Proc. 14th Int. Conf. Machine Learning, (pp. 143-151).
16. John, G. H., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Proc. 11th Int. Machine Learning Conf. (pp. 121-129).
17. Kendall, W. S. (1991). Convexity and the hemisphere. J. London Math. Soc. 43, 567-576.
18. Kleinberg, J., Papadimitriou, C. H., & Raghavan, P. (1998). A microeconomic view of data mining. Data Mining and Knowledge Discovery, 2/4, 311-324.
19. Koller, D., & Sahami, M. (1996). Towards optimal feature selection. In Proc. 13th Int. Conf. Machine Learning. (pp. 284-292), Bari, Italy.
20. Mitra, M., Buckley, C., Singhal, A., & Cardie, C. (1997). An analysis of statistical and syntactic phrases. In Proc. RIAO97: Computer-Assisted Inform. Searching on the Internet (pp. 200-214). Montreal, Canada.
21. Mladenić, D., & Grobelnik, M. (1998). Word sequences as features in text-learning. In Proc. 7th Electrotech. Comput. Sci. Conf. ERK'98 (pp. 145-148). Ljubljana, Slovenia.
22. Modha, D. S., & Spangler, W. S. (2000). Clustering hypertext with applications to web searching. In Proc. ACM Hypertext Conf. (pp. 143-152), San Antonio, TX.
23. Nelder, J., & Mead, R. (1965). A simplex method for function minimization, Computer Journal, 7, 308.
24. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1993). Numerical Recipes in C. New York: Cambridge University Press.
25. Sabin, M. J., & Gray, R. M. (1986). Global convergence and empirical consistency of the generalized Lloyd algorithm, IEEE Trans. Inform. Theory, 32:2, 148-155.
26. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Inform. Proc. & Management (pp. 513-523).
27. Salton, G., & McGill, M. J. (1983), Introduction to Modern Retrieval, McGraw-Hill Book Company.
28. Salton, G., Yang, C. S., & Yu, C. T. (1975). A theory of term importance in automatic text analysis, J. Amer. Soc. Inform. Sci., 26:1, 33-44.
29. Singhal, A., Buckley, C., Mitra, M., & Salton, G. (1996), Pivoted document length normalization. In Proc. ACM SIGIR (pp. 21-29).
30. Smeaton, A. F., & Kelledy, F. (1998), User-chosen phrases in interactive query information for information retrieval. In Proc. 20th BCS-IRSG Colloquium, Springer-Verlag Electronic Workshops in Comput., Grenoble, France.
31. Talavera, L. (1999). Feature selection as a preprocessing step for hierarchical clustering. In Proc. 16th Int. Machine Learning Conf. (pp. 389-397), Bled, Slovenia.
32. Vaithyanathan, S., & Dom, B. (1999). Model selection in unsupervised learning with applications to document clustering. In Proc. 16th Int. Machine Learning Conf. Bled, Slovenia.
33. Wettschereck, D., Aha, D. W., & Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11, 273-314.
34. Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Inform. Retrieval J., 1:1/2, 67-88.