Cluster quality indexes for symbolic classification - An examination

Studies in Classification, Data Analysis, and Knowledge Organization

Volumn , Issue , 2007, Pages 31-38

  Dudek, Andrzej ^a  

^a WROCLAW UNIVERSITY OF ECONOMICS   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION ANALYSIS;

CLASSIFICATION ALGORITHM; CLUSTER QUALITIES; CLUSTERING QUALITY; DATA MATRICES; SYMBOLIC CLASSIFICATIONS; SYMBOLIC DATA;

DATA HANDLING;

EID: 65449167709     PISSN: 14318814     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-70981-7_4     Document Type: Conference Paper

References (22)

1. BAKER, F.B. and HUBERT, L.J. (1975): Measuring the Power of Hierarchical Cluster Analysis. Journal of the American Statistical Association, 70, 349, 31-38.
2. BALL, F.B. and HALL, D.J. (1965): ISODATA, A Novel Method of Data Analysis and Pattern Classification. Tech. Rep. NTIS No.AD 699616, Stanford Research Institute, Menlo Park.
3. BOCK, H.-H. and DIDAY, E. (2000): Analysis of Symbolic Data. Explanatory Methods for Extracting Statistical Information from Complex Data. Springer, Berlin.
4. CALIŃSKI, R.B. and HARABASZ, J. (1974): A Dendrite Method for Cluster Analysis. Communications in Statistics, 3, 1-27.
5. CHAVENT, M., DE CARVALHO, F.A.T., VERDE, R. and LECHEVALLIER, Y. (2003): Trois Nouvelle Méthodes de Classification Automatique de Données Symboliques de Type Intervalle. Revue de Statistique Appliquée, LI 4, 5-29.
6. DIDAY, E. (2002): An Introduction to Symbolic Data Analysis and the SODAS Software. J.S.D.A., International EJournal.
7. FRIEDMAN, H.P. and RUBIN, J. (1967): On Some Invariant Criteria for Grouping Data. Journal of the American Statistical Association, 62, 1159-1178.
8. GORDON, A.D. (1999): Classification, Chapman & Hall/CRC, London.
9. HARDY, A. (2005): Validation of Unsupervised Symbolic Classification. Proceedings of ASMDA 2005 Conference. Available at URL: http://asmda2005.enst- bretagne.fr/IMG/pdf/proceedings/379.pdf.
10. HARTIGAN, J.A. (1975): Clustering Algorithms. New York, Wiley.
11. HUBERT, L.J. (1974): Approximate Evaluation Technique for the Single-link and Complete-link Hierarchical Clustering Procedures. Journal of the American Statistical Association, 69, 347, 698-704.
12. HUBERT, L.J. and LEVINE, J.R. (1976): Evaluating Object Set Partitions: Free Sort Analysis and Some Generalizations. Journal of Verbal Learning and Verbal Behaviour, 15, 549-570.
13. KAUFMAN, L. and ROUSSEEUW, P.J. (1990): Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
14. KRZANOWSKI,W.J. and LAI, Y.T. (1988): A Criterion for Determining the Number of Groups in a Data Set Using Sum of Squares Clustering. Biometrics, 44, 23-34.
15. MARRIOT, F.H. (1971). Practical Problems in a Method of Cluster Analysis. Biometrics, 27, 501-514.
16. MCQUITTY, L.L. (1966): Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825-831.
17. MILLIGAN, G.W. and COOPER, M.C. (1985): An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrika, 2, 159-179.
18. RATKOVSKI, D.A. and LANCE, G.N. (1978) A Criterion for Determining a Number of Groups in a Classification. Australian Computer Journal, 10, 115-117.
19. ROUSSEEUW, P.J. (1987): Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
20. SCOTT, A.J. and SYMONS, M.J. (1971) Clustering Methods Based on Likelihood Ratio Criteria. Biometrics, 27, 387-397.
21. VERDE, R. (2004): Clustering Methods in Symbolic Data Analysis. In: D. Banks et al. (Eds.): Classification, Clustering and Data Mining Applications, Springer, Berlin, 299-318.
22. WEINGESSEL, A., DIMITRIADOU, A. and DOLNICAR, S. (1999): An Examination Of Indexes For Determining The Number Of Clusters In Binary Data Sets. Available at URL: http://www.wu-wien.ac.at/am/wp99.htm#29.