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One of the most important issues of One-Class Classification (OCC) algorithm is how to capture the characteristics of the positive class. Existing structural or clustering based ensemble OCC algorithms build description models for every cluster of the training dataset. However, the introduction of clustering algorithm also causes some problems, such as the determination of the number of clusters and...
Supervised clustering is an emerging area of machine learning, where the goal is to find class-uniform clusters. However, typical state-of-the-art algorithms use a fixed number of clusters. In this work, we propose a variation of a non-parametric Bayesian modeling for supervised clustering. Our approach consists of modeling the clusters as a mixture of Gaussians with the constraint of encouraging...
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