Approximate clustering in very large relational data

International Journal of Intelligent Systems

Volumn 21, Issue 8, 2006, Pages 817-841

  Bezdek, James C ^a   Hathaway, Richard J ^b   Huband, Jacalyn M ^a   Leckie, Christopher ^c   Kotagiri, Ramamohanarao ^c  

^a UNIVERSITY OF WEST FLORIDA   (United States)

^b GEORGIA SOUTHERN UNIVERSITY   (United States)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; DATA STRUCTURES; DATABASE SYSTEMS; SAMPLING; STATISTICAL METHODS; VECTORS;

FUZZY C-MEANS (FCM); GOODNESS OF FIT; PROGRESSIVE SAMPLING; RELATIONAL DATA;

FUZZY CONTROL;

EID: 33746922014     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.20162     Document Type: Article

References (28)

1. Huber P. Massive data workshop: The morning after. In: Massive data sets. Washington, DC: National Academy Press; 1996. pp 169-184.
2. Pal NR, Bezdek JC. Complexity reduction for "large image" processing. IEEE Trans Syst Man Cybern 2002;B32:598-611.
3. Hathaway RJ, Bezdek JC. Extending fuzzy and probabilistic clustering to very large data sets. Comput Stat Data Anal 2006, in press.
4. Bezdek JC, Keller JM, Krishnapuram R, Pal N. Fuzzy models and algorithms for pattern recognition and image processing. Norwell, MA: Kluwer: 1999.
5. Taskar B, Segal E, Koller D. Probabilistic clustering in relational data. In: 17th Int Joint Conf on Artificial Intelligence. Seattle, Washington; 2001. pp 870-876.
6. Provost F, Jensen D, Oates T. 1999. Efficient progressive sampling. In: Proc 5th Conf on Knowledge Discovery and Data Mining. New York: ACM Press; 1999. pp 23-32.
7. Meek C, Thiesson B, Heckerman D. The learning curve sampling method applied to model based clustering. J Mach Learn Res 2002;2:397-418.
8. Bradley P, Fayyad U, Reina C. Scaling clustering algorithms to large databases. In: Proc Fourth Int Conf on Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press; 1998. pp 9-15.
9. Bradley P, Fayyad U, Reina C. Scaling EM (expectation-maximization) clustering to large databases. Technical Report MSR-TR-98-35. Redmond, WA: Microsoft Research; 1998.
10. Zhang T, Ramakrishnan R, Livny M. BIRCH: An efficient data clustering method for very large databases. In: Proc ACM SIGMOD Int Conf on Management of Data. New York: ACM Press; 1996. pp 103-114.
11. Ganti V, Ramakrishnan R, Gehrke J, Powell AL, French JC. Clustering large data sets in arbitrary metric spaces. In: Proc 15th Int Conf on Data Engineering. Los Alamitos, CA: IEEE CS Press; 1999. pp 502-511.
12. Farnstrom F, Lewis J, Elkan C. Scalability for clustering algorithms revisited. SIGKKD Explorations 2000;2:1-7.
13. Ng RT, Han J. Efficient and effective clustering methods for spatial data mining. In: Proc 20th Int Conf on Very Large Databases. San Francisco, CA: Morgan Kauffman; 1994. pp 144-155.
14. Guha S, Rastogi R, Shim K. CURE: An efficient clustering algorithm for large databases. In: Proc ACM SIGMOD Int Conf on Management of Data; 1998. pp 73-84.
15. Fayyad U, Smyth P. From massive data sets to science catalogs: Applications and challenges. In: Kettenring J, Pregibon D, editors. Proc Workshop on Massive Data Sets. Washington, DC: National Research Council; 1996.
16. Domingos P, Hulten G. A general method for scaling up machine learning algorithms and its application to clustering. In: Proc 18th Int Conf on Machine Learning; 2001. pp 106-113.
17. Uma Shankar B, Pal NR. FFCM: An effective approach for large data sets. In: Proc Third Int Conf on Fuzzy Logic, Neural Nets, and Soft Computing, Iizuka, Fukuoka, Japan; 1994. p 332.
18. Cheng TW, Goldgof DB, Hall LO. Fast clustering with application to fuzzy rule generation. In: Proc IEEE Int Conf on Fuzzy Systems, Tokyo, Japan; 1995. pp 2289-2295.
19. Cannon RL, Dave JV, Bezdek JC. Efficient implementation of the fuzzy c-means algorithm. IEEE Trans Pattern Anal Mach Intell 1986;8:248-255.
20. Eschrich S, Ke J, Hall LO, Goldgof DB. Fast accurate fuzzy clustering through data reduction. IEEE Trans Fuzzy Syst 2003;11:262-269.
21. Kolen JF, Hutcheson T. Reducing the time complexity of the fuzzy c-means algorithm. IEEE Trans Fuzzy Syst 2002;10:263-267.
22. Dunn JC. Indices of partition fuzziness and the detection of clusters in large data sets. In: Gupta MM, editor. Fuzzy automata and decision processes. New York: Elsevier; 1976.
23. Hathaway RJ, Bezdek JC. NERF c-means: Non-Euclidean relational fuzzy clustering. Pattern Recogn 1994;27:429-437.
24. Pal NR, Keller JM, Mitchell JA, Popescu M, Huband J, Bezdek JC. Gene ontology-based knowledge discovery through fuzzy cluster analysis. J Neural Parallel Sci Comput 2005:13:337-362.
25. Bezdek JC, Hathaway RJ. VAT: A tool for visual assessment of (cluster) tendency. In: Proc Int Joint Conf on Neural Networks 2002. Piscataway, NJ: IEEE Press; 2002. pp 2225-2230.
26. Hathaway RJ, Bezdek JC. Visual cluster validity for prototype generator clustering models. Pattern Recogn Lett 2003;24:1563-1569.
27. Huband J, Bezdek JC, Hathaway RJ. bigVAT: Visual assessment of cluster tendency for large data sets. Pattern Recogn 2005;38:1875-1886.
28. Bezdek JC, Hathaway RJ. Convergence of alternating optimization. Neural Parallel Sci Comput 2003;11:351-368.