Towards understanding hierarchical clustering: A data distribution perspective

Neurocomputing

Volumn 72, Issue 10-12, 2009, Pages 2319-2330

  Wu, Junjie ^a   Xiong, Hui ^b   Chen, Jian ^c  

^a BEIHANG UNIVERSITY   (China)

^b RUTGERS UNIVERSITY   (United States)

^c School of Economics and Management   (China)

Author keywords

Coefficient of variation (CV); F measure; Hierarchical clustering; Measure normalization; Unweighted pair group method with arithmetic mean (UPGMA)

Indexed keywords

COEFFICIENT OF VARIATION (CV); F-MEASURE; HIERARCHICAL CLUSTERING; MEASURE NORMALIZATION; UNWEIGHTED PAIR GROUP METHOD WITH ARITHMETIC MEAN (UPGMA);

CLUSTER ANALYSIS;

CLUSTERING ALGORITHMS;

ACCURACY; ALGORITHM; ARTICLE; CLUSTER ANALYSIS; DISPERSION; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; NOISE REDUCTION; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SIMULATION; VALIDATION PROCESS;

EID: 67349165851     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.12.011     Document Type: Article

References (42)

1. Borg I., and Groenen P. Modern Multidimensional Scaling-Theory and Applications (1997), Springer, Berlin
2. Bouguettaya A., and Le Viet Q. Data clustering analysis in a multidimensional space. Information Sciences 112 1-4 (1998) 267-295
3. DeGroot M., and Schervish M. Probability and Statistics. third ed. (2001), Addison-Wesley, Reading, MA
4. J.W. Demmel, Applied Numerical Linear Algebra, Society for Industrial & Applied Mathematics, Philadelphia, PA, 1997.
5. J. Ghosh, Scalable Clustering Methods for Data Mining, Handbook of Data Mining, Lawrence Ealbaum Assoc, 2003.
6. Guha S., Rastogi R., and Shim K. Cure: an efficient clustering algorithm for large databases. Proceedings of 1998 ACM SIGMOD International Conference on Management of Data (June 1998) 73-84
7. Halkidi M., Batistakis Y., and Vazirgiannis M. Cluster validity methods: Part i. SIGMOD Record 31 2 (2002) 40-45
8. Halkidi M., Batistakis Y., and Vazirgiannis M. Clustering validity checking methods: Part ii. SIGMOD Record 31 3 (2002) 19-27
9. Han E.-H., Boley D., Gini M., Gross R., Hastings K., Karypis G., Kumar V., Mobasher B., and Moore J. WebACE: a web agent for document categorization and exploration. Proceedings of the 2nd International Conference on Autonomous Agents (1998)
10. Hersh W., Buckley C., Leone T.J., and Hickam D. OHSUMED: an interactive retrieval evaluation and new large test collection for research. Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval (July 1994) 192-201
11. Hirano S., Sun X., and Tsumoto S. Comparison of clustering methods for clinical databases. Information Sciences 159 3-4 (2004) 155-165
12. Hruschka E.R., Campello R.J.G.B., and de Castro L.N. Evolving clusters in gene-expression data. Information Sciences 176 13 (2006) 1898-1927
13. Hubert L., and Arabie P. Comparing partitions. Journal of Classification 2 (1985) 193-218
14. Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1998), Prentice-Hall, Englewood Cliffs, NJ
15. Jarvis R.A., and Patrick E.A. Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers C-22 11 (1973) 1025-1034
16. Jolliffe I.T. Principal Component Analysis. second ed. (2002), Springer, Berlin
17. Karypis G., Han E.-H., and Kumar V. Chameleon: a hierarchical clustering algorithm using dynamic modeling. IEEE Computer 32 8 (1999) 68-75
18. G. Karypis, Cluto-software for clustering high-dimensional datasets, version 2.1.1, 2008 〈http://glaros.dtc.umn.edu/gkhome/views/cluto〉.
19. Larsen B., and Aone C. Fast and effective text mining using linear-time document clustering. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August 1999) 16-22
20. D. Lewis. Reuters-21578 text categorization text collection 1.0, 2008 〈http://www.research.att.com/lewis〉.
21. Li J., Tao D., Hu W., and Li X. Kernel principle component analysis in pixels clustering. Proceedings of The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (2005) 786-789
22. J. Li, H. Liu. Kent ridge biomedical data set repository, 2008 〈http://sdmc.i2r.a-star.edu.sg/rp/〉.
23. Murtagh F. Clustering Massive Data Sets, Handbook of Massive Data Sets (2000), Kluwer Academic Publishers, Dordrecht
24. D.J. Newman, S. Hettich, C.L. Blake, C.J. Merz, UCI repository of machine learning databases, 1998.
25. Pang Y., Tao D., Yuan Y., and Li X. Binary two-dimensional PCA. IEEE Transactions on Systems, Man, and Cybernetics, Part B 38 4 (2008) 1176-1180
26. Porter M.F. An algorithm for suffix stripping. Program 14 3 (1980) 130-137
27. Van Rijsbergen C.J. Information Retrieval. second ed. (1979), Butterworths, London
28. Ryu T.-W., and Eick C.F. A database clustering methodology and tool. Information Sciences 171 1-3 (2005) 29-59
29. Sneath P.H., and Sokal R.R. Numerical Taxonomy (1973), Freeman, San Francisco, CA
30. M. Steinbach, G. Karypis, V. Kumar, A comparison of document clustering techniques, in: Workshop on Text Mining, the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2000.
31. Tan P.-N., Steinbach M., and Kumar V. Introduction to Data Mining (2005), Addison-Wesley, Reading, MA
32. B. Tang, M. Shepherd, M.I. Heywood, X. Luo, Comparing dimension reduction techniques for document clustering, in: Canadian Conference on Artificial Intelligence, 2005, pp. 292-296.
33. Tao D., Li X., Wu X., and Maybank S. Geometric mean for subspace selection in multiclass classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2008)
34. TREC, Text retrieval conference, 2008 〈http://trec.nist.gov〉.
35. Xiong H., Pandey G., Steinbach M., and Kumar V. Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering 18 3 (2006) 304-319
36. Xiong H., Tan P., and Kumar V. Mining strong affinity association patterns in data sets with skewed support distribution. Proceedings of the 3rd IEEE International Conference on Data Mining (2003) 387-394
37. Xiong H., Tan P.-N., and Kumar V. Hyperclique pattern discovery. Data Mining and Knowledge Discovery Journal 13 2 (2006) 219-242
38. Xiong H., Wu J., and Chen J. K-means clustering versus validation measures: a data distribution perspective. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August 2006) 779-784
39. Zhang T., Ramakrishnan R., and Livny M. Birch: an efficient data clustering method for very large databases. Proceedings of 1996 ACM SIGMOD International Conference on Management of Data (June 1996) 103-114
40. Zhang T., Tao D., and Yang J. Discriminative locality alignment. Proceedings of the 10th European Conference on Computer Vision (ECCV) (2008) 725-738
41. Y. Zhao, G. Karypis, Hierarchical clustering algorithms for document datasets, Technical Report #03-027, University of Minnesota, Minneapolis, MN, 2003.
42. Zhao Y., and Karypis G. Criterion functions for document clustering: experiments and analysis. Machine Learning 55 3 (2004) 311-331