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.
Keywords
- Similarity Function
- Qualitative Feature
- Comparison Function
- Quantitative Feature
- Aggregation Step
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.
This work was financially supported by CONACyT (México) through the project J38707-A.
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References
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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
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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
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