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 A_i can be determined by the way in which the values of the other attributes A_j are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of A_i a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes A_j. 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.
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