MACLAW: A modular approach for clustering with local attribute weighting

• Published in: Pattern recognition letters Vol. 27; no. 11; pp. 1299 - 1306
• Main Authors: Blansché, A., Gançarski, P., Korczak, J.J.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2006
• Subjects: Clustering criterion; Complex data; Cooperative coevolution; Feature weighting; Modular clustering; Clustering criterion; Complex data; Modular clustering; Feature weighting; Cooperative coevolution
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2005.07.027

This paper presents a new process for modular clustering of complex data, like remote sensing images. This method performs feature weighting in a wrapper approach. The proposed method is a modular clustering method that combines several extractors, which are local specialists, each one extracting one cluster only and using different feature weights. A new clustering quality criterion, adapted to independent clusters, is defined. The weight learning is performed through a cooperative coevolution algorithm, where each species represents one of the clusters to be extracted. A set of extracted clusters forms a partial soft clustering but can be transformed in a classic hard clustering. Some tests, on datasets from the UCI repository and on hyperspectral remote sensing image, have been performed and show the validity of the approach.