Abstract
In this paper, a new approach of soft subspace clustering is proposed. It is based on the estimation of the clusters centres using a multi-class support vector machine (SVM). This method is an extension of the ESSC algorithm which is performed by optimizing an objective function containing three terms: a weighting within cluster compactness, entropy of weights and a weighting between clusters separations. First, the SVM is used to compute initial centres and partition matrices. This new developed formulation of the centres is integrated in each iteration to yield new centres and membership degrees. A comparative study has been conducted on UCI datasets and different image types. The obtained results show the effectiveness of the suggested method.
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Boulemnadjel, A., Hachouf, F. (2013). Estimating Clusters Centres Using Support Vector Machine: An Improved Soft Subspace Clustering Algorithm. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_30
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DOI: https://doi.org/10.1007/978-3-642-40261-6_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
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