| 標題主旨 |
講題: Maximum likelihood clustering via a multimodal probability model
演講者：楊敏生 教授(中原大學應用數學系)
Abstract : In this talk, we consider a new proposed multimodal probability model for clustering. Based on the probability model, we could use the maximum likelihood (ML) estimation to establish an ML clustering approach. We show that the partitional clustering can be equivalent to the ML clustering under the multimodal probability model. This ML clustering approach can lead to most partitional clustering algorithms, such as k-means, fuzzy c-means (FCM), possibilistic c-means (PCM), mean shift, classification ML and latent class methods. Furthermore, we construct two ML clustering frameworks based on the multimodal probability model for producing new clustering algorithms. One framework can induce penalized-type clustering algorithms. Another framework induces entropy-type clustering algorithms. Several numerical and real data sets are made for comparisons. These experimental results show that our new constructions based on the proposed ML clustering can produce useful and effective clustering algorithms.
時間：2010年11月9日(星期二) 14：00 ∼ 15：30
地點：推廣大樓3樓9313室
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