Abstract
A number of mixture models of local Principal Component Analysis (PCA) have been developed to analyze data distributed in space. Most of these models require the users to determine the number of the local PCA models, i.e., the number of clusters for clustering analysis. This is not a reasonable requirement in practical applications. This paper proposes an automatic clustering algorithm to analyze data based on a competition model of probabilistic PCA. Without identifying the number of clusters in advance, the algorithm automatically evolves to partition a given data set into some small clusters in terms of the empirical rule of Gaussian distribution. It is shown the algorithm will not only group data but also can explore the hierarchical structure of a given data.
This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education under Grant 2010081110053.
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Li, Y., Lv, J.C., Li, X. (2015). An Automatic Clustering Algorithm Based on a Competition Model of Probabilistic PCA. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_6
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DOI: https://doi.org/10.1007/978-3-319-13359-1_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13358-4
Online ISBN: 978-3-319-13359-1
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