Performance evaluation of compromise conditional Gaussian networks for data clustering

Abstract This paper is devoted to the proposal of two classes of compromise conditional Gaussian networks for data clustering as well as to their experimental evaluation and comparison on synthetic and real-world databases. According to the reported results, the models show an ideal trade-off between efficiency and effectiveness, i.e., a balance between the cost of the unsupervised model learning process and the quality of the learnt models. Moreover, the proposed models are very appealing due to their closeness to human intuition and computational advantages for the unsupervised model induction process, while preserving a rich enough modeling power.

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