A genetic algorithm for cluster analysis

Intelligent Data Analysis

Volumn 7, Issue 1, 2003, Pages 15-25

  Hruschka, Eduardo R ^a   Ebecken, Nelson F F ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

clustering; data mining; genetic algorithms

Indexed keywords

CHROMOSOMES; CLUSTER ANALYSIS; DATA MINING; GENETIC ALGORITHMS;

CLUSTERING; CLUSTERING GENETIC ALGORITHMS; CONTEXT SENSITIVE; ENCODING SCHEMES; GENETIC OPERATORS; NUMBER OF CLUSTERS; OBJECTIVE FUNCTIONS; SILHOUETTE WIDTH;

CLUSTERING ALGORITHMS;

EID: 2942695509     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2003-7103     Document Type: Article

References (32)

1. P. Arabie and L.J. Hubert, An Overview of Combinatorial Data Analysis (Chapter 1), in: Clustering and Classification, P. Arabie, L.J. Hubert and G. DeSoete, eds, World Scientific, 1999.
2. S. Levine, I. Pilowski and D.M. Boulton, The classification of depression by numerical taxonomy, The British Journal of Psychiatry 115 (1969), 937-945.
3. P.E. Green, R.E. Frank and P.J. Robinson, Cluster analysis in test market selection, Management Science B13(8), 387-400.
4. F.R. Hodson, Numerical Typology and prehistoric archaeology, in: Mathematics in the Archaeological and Historical Sciences, F.R. Hodson, D.G. Kendall and P.A. Tautu, eds, University Press, Edimburg, 1971.
5. E. Levrat, V. Bombardier, M. Lamotte and J. Bremont, Multi-level image segmentation using fuzzy clustering and local membership variations detection, IEEE International Conference on Fuzzy Systems, IEEE Press, NY, 1992, pp. 221-228.
6. Y. Reich and S. Fenves, The formation and use of abstract concepts in design, in: Concept Formation: Knowledge and Experience in Unsupervised Learning, D. Fisher, M. Pazzani and P. Langley, eds, Morgan Kaufman, San Mateo, 1991, pp. 323-354.
7. M. Funk, R.D. Appel, D. Roch and D. Hochstrasser et al., Knowledge acquisition in expert system assisted diagnosis: a machine learning approach, Proceedings of the AIME-87, Springer Verlag, 1987, pp. 99-103.
8. J.P. Bigus, Data Mining with Neural Networks, (First Edition), McGraw-Hill, USA, 1996.
9. R.M. Cole, Clustering with Genetic Algorithms, Master of Science Thesis, Department of Computer Science, University of Western Australia, 1998.
10. L. Kaufman and R.J. Rousseeuw, Finding Groups in Data-An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons Inc., 1990.
11. G.W. Miligan, Clustering Validation: Results and Implications for Applied Analyses, in: Clustering and Classification, P. Araboe, L.J. Hubert and G. de Soete, eds, World Scientific, Publishing Co. Pte. Ltd., Singapore, 1996, pp. 341-373.
12. G.L. Liu, Introduction to combinatorial mathematics, McGraw Hill, 1968.
13. E. Falkenauer, Genetic Algorithms and Grouping Problems, John Wiley & Sons, 1998.
14. Y. Park and M. Song, AGenetic Algorithm for Clustering Problems, Proceedings of the Genetic Programming Conference, University of Wisconsin, July 1998.
15. E.R. Hruschka and N.F.F. Ebecken, Credit approval by a clustering genetic algorithm, in: Data Mining II, N. Ebecken and C.A. Brebbia, eds, Proceedings of the Data Mining 2000 Conference, Cambridge University, Cambridge, UK, July 2000, pp. 403-413.
16. E.R. Hruschka and N.F.F. Ebecken, Applying a Clustering Genetic Algorithm for Extracting Rules from a Supervised Neural Network, Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, July 2000.
17. E.R. Hruschka and N.F.F. Ebecken, Using a Clustering Genetic Algorithm for Rule Extraction from Artificial Neural Networks, Proceedings of The First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, San Antonio, TX, USA, May 2000.
18. E.R. Hruschka and N.F.F. Ebecken, A Clustering Genetic Algorithm for Extracting Rules from Supervised Neural Network Models in Data Mining Tasks, in: International Journal of Computers, Systems and Signals, Special Issue: Knowledge Discovery from Structured and Unstructured Data, (Vol. 1), (No. 1), A.P. Engelbrecht, ed., December 2000, pp. 17-29.
19. R. Kothari and D. Pitts, On finding the number of clusters, Pattern Recognition Letters (No. 20), Elsevier, 1999, pp. 405-416.
20. E. Nakamura and N. Kehtarnavaz, Determining number of clusters and prototype locations via multi-scale clustering, Pattern Recognition Letters, (No. 19), Elsevier, 1998, pp. 1265-1283.
21. H. Bock, Probability Models and Hypotheses Testing in Partitioning Cluster Analysis, in: Clustering and Classification, P. Arabie, L.J. Hubert and G. de Soete, eds, World Scientific, Publishing Co. Pte. Ltd., Singapore, 1996, pp. 377-454.
22. R. Maitra, Clustering Massive Datasets, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, www.math.umbc.edu/̃maitra/ publications.html, in preparation, 1998.
23. D. Boley and V. Borst, Unsupervised Clustering: A Fast Scalable Method for Large Datasets, Technical Report IIS-9811229, Department of Computer Science and Engineering, University of Minnesota.
24. C. Fraley and A.E. Raftery, HowMany Clusters? Which Clustering Method? Answers via Model-Based Cluster Analysis, Technical Report No. 329, Department of Statistics, University of Washington, Seattle, USA, Feb. 1998.
25. T. Calinski and J. Harabasz, A dendrite method for cluster analysis, Communications in statistics 3(1) (1974), 1-27.
26. D.L. Davies and D.W. Bouldin, A cluster separation measure, IEEE Transactions on pattern analysis and machine intelligence PAMI-1(2) (1979), 224-227.
27. B.S. Everitt, Cluster Analysis, (third edition), Halsted Press, 1993.
28. G.W. Milligan and M.C. Cooper, An examination of procedures for determining the number of clusters in a data set, Psychometrika 50(2) (1985), 159-179.
29. M.J. Symons, Clustering criteria and multivariate normal mixtures, Biometrics 37 (1995), 655-659.
30. L. Davis, Handbook of Genetic Algorithms, International Thompson Computer Press, 1996.
31. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley Longman, Inc., 1989.
32. C.J. Merz and P.M. Murphy, UCI Repository of Machine Learning Databases, [http://www.ics.uci.edu]. Irvine, CA, University of California, Department of Information and Computer Science.