Abstract
We present a genetic algorithm that deals with document clustering. This algorithm calculates an approximation of the optimum k value, and solves the best grouping of the documents into these k clusters. We have evaluated this algorithm with sets of documents that are the output of a query in a search engine. The experiments show that, most of the times, our genetic algorithm obtains better values of the fitness function than the well known Calinski and Harabasz stopping rule, and takes less time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Calinski, T., Harabasz, J.: A Dendrite Method for Cluster Analysis. Communications in Statistics 3(1), 1–27 (1974)
Chu, S.C., Roddick, J.F., Pan, J.S.: An Incremental Multi-Centroid, Multi-Run Sampling Scheme for k-medoids-based Algortihms-Extended Report. In: Proceedings of the Third International Conference on Data Mining Methods and Databases, Data Mining III, pp. 553–562 (2002)
Estivill-Castro, V., Murray, A.T.: Spatial Clustering for Data Mining with Genetic Algorithms. In: Proceedings of the International ICSC Symposium on Engineering of Intelligent Systems, EIS 1998 (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Longman, Inc., Amsterdam (2002)
Gordon, A.D.: Classification. Chapman & Hall/CRC (1999)
Holland, J.H.: Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor (1975)
Lucasius, C.B., Dane, A.D., Kateman, G.: On k-medoid clustering of large data sets with the aid of Genetic Algorithm: background, feasibility and comparison. In: Analytica Chimica Acta, vol. 283(3), pp. 647–669. Elsevier Science Publishers B.V., Amsterdam (1993)
Makagonov, P., Alexandrov, M., Gelbukh, A.: Selection of typical documents in a document flow. In: Advances in Communications and Software Technologies, pp. 197–202. WSEAS Press (2002)
Merz, P., Zell, A.: Clustering Gene Expresion Profiles with Memetic Algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 811–820. Springer, Heidelberg (2002)
Michalewicz, Z.: Genetic algorithms+data structures=evolution programs. Springer Comp., Heidelberg (1996)
Milligan, G.W., Cooper, M.C.: An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrik 58(2), 159–179 (1985)
Murthy, C.A., Chowdhury, N.: In search of Optimal Clusters Using Genetic Algorithms. Pattern Recognition Letters 17(8), 825–832 (1996)
Sarkar, M., Yegnanarayana, B., Khemani, D.: A clustering algorithm using an evolutionary programming-based approach. Pattern Recognition Letters 18, 975–986 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Casillas, A., de Lena, M.T.G., Martínez, R. (2003). Document Clustering into an Unknown Number of Clusters Using a Genetic Algorithm. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2003. Lecture Notes in Computer Science(), vol 2807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39398-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-39398-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20024-6
Online ISBN: 978-3-540-39398-6
eBook Packages: Springer Book Archive