A novel approach to unsupervised robust clustering using genetic niching

Abstract

We present a new unsupervised robust clustering algorithm that can successfully find dense areas (clusters) in feature space and determine their number. The clustering problem is converted to a multimodal function optimization problem within the context of genetic niching. The niche peaks, which constitute the final cluster centers, are identified based on deterministic crowding (DC). The problem of crossover interactions in DC is eliminated by restricting the mating to members of the same niche only. Finally, the correct number of niche peaks or cluster centers is extracted from the final population. Genetic optimization makes our approach much less prone to suboptimal solutions than other objective function based approaches, and frees it from the necessity of an analytical derivation of the prototypes. As a result, our approach can handle a vast array of general subjective, even non-metric dissimilarities, and is thus useful in many applications such as Web and data mining. Additionally, the use of robust weights makes it less sensitive to the presence of noise than most traditional unsupervised clustering techniques.