Clustering of symbolic data through a dissimilarity volume based measure

The recording of symbolic data has become a common practice with the advances in database technologies. This paper shows hard and fuzzy relational clustering in order to partition symbolic data. These methods optimize objective functions based on a dissimilarity function. The distance used is a volume based measure and may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach.

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Year of publication:	2008
Authors:	Silva, K.P. ; De A. T. De Carvalho, Francisco ; Csernel, Marc
Institutions:	Université Paris-Dauphine (Paris IX)
Subject:	fuzzy set theory \| pattern clustering \| relational databases \| clustering methods \| database technologies

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Series:	Economics Papers from University Paris Dauphine.
Type of publication:	Book / Working Paper
Notes:	Published in Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on,
Classification:	C02 - Mathematical Methods ; C69 - Mathematical Methods and Programming. Other ; C89 - Data Collection and Data Estimation Methodology; Computer Programs. Other
Source:	RePEc - Research Papers in Economics

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