Finding centroid clusterings with entropy-based criteria

Knowledge and Information Systems

Volumn 10, Issue 4, 2006, Pages 505-514

  Hu, Tianming ^a   Sung, Sam Yuan ^b  

^a DONGGUAN UNIVERSITY OF TECHNOLOGY   (China)

^b South Texas College   (United States)

Author keywords

Centroid clustering; Cluster analysis; Conditional entropy; Consensus clustering; Metric distance function

Indexed keywords

CLUSTER ANALYSIS;

CANDIDATE SETS; CENTROID CLUSTERING; COMBINING METHOD; CONDITIONAL ENTROPY; CONSENSUS CLUSTERING; DISTANCE FUNCTIONS; FOLLOWING PROBLEM; METRIC DISTANCES;

ENTROPY;

EID: 33750573084     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-006-0017-7     Document Type: Article

References (19)

1. Dietterich TG (2001) Ensemble methods in machine learning. In: Proceedings of the 2nd international workshop on multiple classifier systems, pp 1-15
2. Fayyad UM, Reina C, Bradley PS (1998) Initialization of iterative refinement clustering algorithms. In: Proceedings of the 14th international conference on machine learning, pp 194-198
3. Fisher D (1996) Iterative optimization and simplification of hierarchical clusterings. J Artif Intell Res 4:147-180
4. Fred ALN, Jain AK (2002) Evidence accumulation clustering based on the k -means algorithm. In: Proceedings of the joint IAPR international workshops on structural, syntactic, and statistical pattern recognition, pp 442-451
5. Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning, pp 148-156
6. Frossyniotis D, Likas A, Stafylopatis A (2004) A clustering method based on boosting. Pattern Recog Lett 25(6):641-654
7. Ghosh J (2002) Multiclassifier systems: Back to the future. In: Proceedings of the 3rd international workshop on multiple classifier systems, pp 1-15
8. Gordon A (1999) Classification, 2nd edn. Chapman and Hall/CRC Press, Boca Raton
9. Grabmeier J, Rudolph A (2002) Techniques of cluster algorithms in data mining. Data Min Knowl Discov 6(4):303-360
10. Hubert LJ, Arabie P (1985) Comparing partitions. J Class 2:63-76
11. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: A review. ACM Comput Surv 31(3):264-323
12. Johnson E, Kargupta H (1999) Collective, hierarchical clustering from distributed, heterogeneous data. In: Large-scale parallel KDD systems. Springer-Verlag, Berlin Heidelberg New York, pp 221-244
13. Kargupta H, Huang W, Johnson E (2001) Distributed clustering using collective principal component analysis. Knowl Inform Syst J 3:422-448
14. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359-392
15. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: An introduction to cluster analysis. Wiley, New York
16. Ross S (1998) A first course in probability, 5th edn. Prentice-Hall, Engelwood Cliffs
17. Schapire R (1990) The strength of weak learnability. Mach Learn 5(2):197-227
18. Sharkey A (1999) Combining artificial neural nets. Springer-Verlag, Berlin Heidelberg New York
19. Strehl A, Ghosh J (2002) Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583-617