文献类型：期刊文献

英文题名：Evaluate the number of clusters in finite mixture models with the penalized histogram difference criterion

作者：Lin, Weilu[1];Wang, Yonghong[1];Zhuang, Yingping[1];Zhang, Siliang[1]

机构：[1]E China Univ Sci & Technol, Sate Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2013

卷号：23

期号：8

起止页码：1052

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20133116552619);WOS:【SCI-EXPANDED(收录号:WOS:000324669000003)】；

基金：Authors acknowledge National Key Basic Research Program of China (973 Program, 2013CB733605), National High Technology Research and Development Program of China (863 Program, 2011AA02A205) and the Initiative Fund for Young Researchers of ECUST (YF0157126) for providing financial support. Authors also acknowledge COFCO Limited for providing the industrial data set.

语种：英文

外文关键词：Finite mixture models; Penalized histogram difference criterion; Gaussian mixtures; Information criteria; EM algorithm

摘要：Aimed at the determination of the number of mixtures for finite mixture models (FMMs), in this work, a new method called the penalized histogram difference criterion (PHDC) is proposed and evaluated with other criteria such as Akaike information criterion (AIC), the minimum message length (MML), the information complexity (ICOMP) and the evidence of data criterion (EDC). The new method, which calculates the penalized histogram difference between the data generated from estimated FMMs and those for modeling purpose, turns out to be better than others for data with complicate mixtures patterns. It is demonstrated in this work that the PHDC can determine the optimal number of clusters of the FMM. Furthermore, the estimated FMMs asymptotically approximate the true model. The utility of the new method is demonstrated through synthetic data sets analysis and the batch-wise comparison of citric acid fermentation processes. (C) 2013 Elsevier Ltd. All rights reserved.