Abstract
The conceptual k-means algorithm consists of two steps. In the first step the clusters are obtained (aggregation step) and in the second one the concepts or properties for those clusters are generated (characterization step). We consider the conceptual k-means management of mixed, qualitative and quantitative, features is inappropriate. Therefore, in this paper, a new conceptual k-means algorithm using similarity functions is proposed. In the aggregation step we propose to use a different clustering strategy, which allows working in a more natural way with object descriptions in terms of quantitative and qualitative features. In addition, an improvement of the characterization step and a new quality measure for the generated concepts are presented. Some results obtained after applying both, the original and the modified algorithms on different databases are shown. Also, they are compared using the proposed quality measure.
This work was financially supported by CONACyT (México) through the project J38707-A.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hanson, S.J.: Conceptual clustering and categorization: bridging the gap between induction and causal models. In: Kodratoff, Y., Michalski, R.S. (eds.) Machine Learning: an artificial intelligence approach, vol. 3, pp. 235–268. Morgan Kaufmann, Los Altos (1990)
Ralambondrainy, H.: A conceptual version of the K-means algorithm. Pattern Recognition Letters 16, 1147–1157 (1995)
Ralambondrainy, H.: A clustering method for nominal data and mixture of numerical and nominal data. In: Proc. First Conf. Internat. Federation of Classification Societies, Aachen (1987)
García Serrano, J.R., Martínez-Trinidad, J.F.: Extension to k-means algorithm for the use of similarity functions. In: 3rd European Conference on Principles of Data Mining and Knowledge Discovery Proceedings, Prague, Czech. Republic, pp. 354–359 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ayaquica-Martínez, I.O., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A. (2005). Conceptual K-Means Algorithm with Similarity Functions. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_39
Download citation
DOI: https://doi.org/10.1007/11578079_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)