Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of the same categorical attribute, since they are not ordered. In this paper, we propose a method to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute Ai can be determined by the way in which the values of the other attributes Aj are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of Ai a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes Aj. We validate our approach on various real world and synthetic datasets, by embedding our distance learning method in both a partitional and a hierarchical clustering algorithm. Experimental results show that our method is competitive w.r.t. categorical data clustering approaches in the state of the art.

Context-Based Distance Learning for Categorical Data Clustering

IENCO, Dino;PENSA, Ruggero Gaetano;MEO, Rosa
2009

Abstract

Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of the same categorical attribute, since they are not ordered. In this paper, we propose a method to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute Ai can be determined by the way in which the values of the other attributes Aj are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of Ai a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes Aj. We validate our approach on various real world and synthetic datasets, by embedding our distance learning method in both a partitional and a hierarchical clustering algorithm. Experimental results show that our method is competitive w.r.t. categorical data clustering approaches in the state of the art.
8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon
Lyon, France
August 31 - September 2, 2009
Advances in Intelligent Data Analysis VIII, 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings
SPRINGER-VERLAG
5772/2009
83
94
9783642039140
http://ida09.liris.cnrs.fr/
D. Ienco; R. G. Pensa; R. Meo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/66894
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