Cluster stability assessment based on theoretic information measures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5197 LNCS, Issue , 2008, Pages 219-226

  Pascual, Damaris ^a   Pla, Filiberto ^b   Sánchez, J Salvador ^b  

^a UNIVERSIDAD DE ORIENTE   (Cuba)

^b UNIVERSITAT JAUME I   (Spain)

Author keywords

Cluster validation; Information theory; Stability indices

Indexed keywords

CLUSTER ANALYSIS; DATA STRUCTURES; FEATURE EXTRACTION; FILE ORGANIZATION; FLOW OF SOLIDS; IMAGE ANALYSIS; INFORMATION THEORY; PATTERN RECOGNITION; STABILITY; SYSTEM STABILITY;

CLUSTER STABILITIES; CLUSTER VALIDATION; CLUSTER VALIDATIONS; CLUSTERING PROCESSES; CLUSTERING SOLUTIONS; INFORMATION MEASURES; NUMBER OF CLUSTERS; REAL DATABASES; SAMPLE SETS; STABILITY INDICES; STRUCTURE MODELS;

CLUSTERING ALGORITHMS;

EID: 55349117161     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85920-8_27     Document Type: Conference Paper

References (13)

1. Bouguessa, M., Wang, S., Sun, H.: An Objective approach to cluster validation. Pattern Recognition Letters 27, 1419-1430 (2006)
2. Ertoz, L., Steinbach, M., Kumar, V.: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. In: Third SIAM International Conference on data Mining (2003)
3. Lange, T., Braun, M.L., Buhmann, J.M.: Stability-Based Validation of Clustering Solutions. Neural Computation 16, 1299-1323 (2004)
4. Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159-179 (1985)
5. Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370-379 (1995)
6. Pascual, D., Pla, F., Sánchez, J.S.: Non Parametric Local Density-based Clustering for Multimodal Overlapping Distributions. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 671-678. Springer, Heidelberg (2006)
7. Sugar, C.: Techniques for clustering and classification with applications to medical problems. PhD Dissertation Stanford University, Stanford (1998)
8. Sugar, C., Lenert, L., Olshen, R.: An application of cluster analysis to health services research: empirically defined health states for depression from the sf-12. Technical Report Stanford University, Stanford (1999)
9. Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Statist Soc. B 63, Part 2, 411-423 (2001)
10. Ben-Hur, A., Guyon, I.: Detecting stable clusters using principal component analysis. In: Brownstein, M., Khodursky, A. (eds.) Methods in Molecular Biology, pp. 159-182. Humana press (2003)
11. Mufti, G.B., Bertrand, P., Moubarki, L.E.: Determining the number of groups from measures of cluster validity. In: ASMDA 2005, pp. 404-414 (2005)
12. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)
13. Li, J.: Divergence measures based on Shannon entropy. IEEE Trans, on Information Theory 37(1), 145-151 (1991)