Abstract
We propose an incremental nonparametric Bayesian approach for clustering. Our approach is based on a Dirichlet process mixture of generalized Dirichlet (GD) distributions. Unlike classic clustering approaches, our model does not require the number of clusters to be pre-defined. Moreover, an unsupervised feature selection scheme is integrated into the proposed nonparametric framework to improve clustering performance. By learning the proposed model using an incremental variational framework, the number of clusters as well as the features weights can be automatically and simultaneously computed. The effectiveness and merits of the proposed approach are investigated on a challenging application namely anomaly intrusion detection.
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Fan, W., Bouguila, N., Sallay, H. (2013). Anomaly Intrusion Detection Using Incremental Learning of an Infinite Mixture Model with Feature Selection. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_35
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DOI: https://doi.org/10.1007/978-3-642-41299-8_35
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