Combining supervised and unsupervised learning for

Authors: Paolo Corsini Beatrice Lazzerini Francesco Marcelloni

Publish Date: 2006/01/28

Volume: 15, Issue: 3-4, Pages: 289-297

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Abstract

Clustering aims to partition a data set into homogenous groups which gather similar objects Object similarity or more often object dissimilarity is usually expressed in terms of some distance function This approach however is not viable when dissimilarity is conceptual rather than metric In this paper we propose to extract the dissimilarity relation directly from the available data To this aim we train a feedforward neural network with some pairs of points with known dissimilarity Then we use the dissimilarity measure generated by the network to guide a new unsupervised fuzzy relational clustering algorithm An artificial data set and a real data set are used to show how the clustering algorithm based on the neural dissimilarity outperforms some widely used possibly partially supervised clustering algorithms based on spatial dissimilarity

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