Context-Based Distance Learning for Categorical Data Clustering

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 res...

• Type: Journal
• Year: 2009
• Where: IDA
• Authors: Dino Ienco, Ruggero G. Pensa, Rosa Meo
• Keywords: Categorical Attribute, Categorical Attribute Ai, Hierarchical Clustering Algorithm, IDA 2009, Information Technology