CLUSTERING OF HETEROGENEOUSLY TYPED DATA WITH SOFT COMPUTING

Angel Kuri-Morales, Luis Enrique Cortes-Berrueco, Daniel Trejo-Baños

Abstract

The problem of finding clusters in arbitrary sets of data has been attempted using different approaches. In most cases, the use of metrics in order to determine the adequateness of the said clusters is assumed. That is, the criteria yielding a measure of quality of the clusters depends on the distance between the elements of each cluster. Typically, one considers a cluster to be adequately characterized if the elements within a cluster are close to one another while, simultaneously, they appear to be far from those of different clusters. This intuitive approach fails if the variables of the elements of a cluster are not amenable to distance measurements, i.e., if the vectors of such elements cannot be quantified. This case arises frequently in real world applications where several variables correspond to categories. The usual tendency is to assign arbitrary numbers to every category: to encode the categories. This, however, may result in spurious patterns: relationships between the variables which are not really there at the offset. It is evident that there is no truly valid assignment which may ensure a universally valid numerical value to this kind of variables. But there is a strategy which guarantees that the encoding will, in general, not bias the results. In this paper we explore such strategy. We discuss the theoretical foundations of our approach and prove that this is the best strategy in terms of the statistical behaviour of the sampled data. We also show that, when applied to a complex real world problem, it allows us to generalize soft computing methods to find the number and characteristics of a set of clusters.

References

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Paper Citation


in Harvard Style

Kuri-Morales A., Cortes-Berrueco L. and Trejo-Baños D. (2011). CLUSTERING OF HETEROGENEOUSLY TYPED DATA WITH SOFT COMPUTING . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 491-494. DOI: 10.5220/0003690304990502


in Bibtex Style

@conference{kdir11,
author={Angel Kuri-Morales and Luis Enrique Cortes-Berrueco and Daniel Trejo-Baños},
title={CLUSTERING OF HETEROGENEOUSLY TYPED DATA WITH SOFT COMPUTING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={491-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003690304990502},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - CLUSTERING OF HETEROGENEOUSLY TYPED DATA WITH SOFT COMPUTING
SN - 978-989-8425-79-9
AU - Kuri-Morales A.
AU - Cortes-Berrueco L.
AU - Trejo-Baños D.
PY - 2011
SP - 491
EP - 494
DO - 10.5220/0003690304990502