Abstract
In clustering, most feature selection approaches account for all the features of the data to identify a single common feature subset contributing to the discovery of the interesting clusters. However, many data can comprise multiple feature subsets, where each feature subset corresponds to the meaningful clusters differently. In this paper, we attempt to reveal a feature partition consisting of multiple non-overlapped feature blocks that each one fits a finite mixture model. To find the desired feature partition, we used a local search algorithm based on a Simulated Annealing technique. During the process of searching for the optimal feature partition, reutilization of the previous estimation results has been adopted to reduce computational cost.
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Namkoong, Y., Joo, Y., Dankel, D.D. (2010). Feature Subset-Wise Mixture Model-Based Clustering via Local Search Algorithm. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_15
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DOI: https://doi.org/10.1007/978-3-642-13059-5_15
Publisher Name: Springer, Berlin, Heidelberg
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