MACLAW: A modular approach for clustering with local attribute weighting

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.