From Penalized Maximum Likelihood to cluster analysis: A unified probabilistic framework of clustering | |
Sun, Xichen ; Cheng, Qiansheng ; Feng, Jufu | |
2007 | |
关键词 | clustering penalized maximum likelihood |
英文摘要 | A unified probabilistic framework (UPF) of partitional clustering algorithms is proposed based on Penalized Maximum Likelihood. Besides Gaussian Mixture model methods, many popular clustering methods, such as Fuzzy c-Means Algorithm (FCM), Attribute Means Clustering (AMC), General c-Means Clustering (GCM), and Deterministic Annealing (DA) Clustering can be explained as special cases within UPF. Furthermore, this UPF framework provides a general approach to design comparatively stable and effectively regularized clustering algorithms.; Computer Science, Artificial Intelligence; SCI(E); EI; 0; ARTICLE; 3; 483-490; 21 |
语种 | 英语 |
出处 | EI ; SCI |
出版者 | international journal of pattern recognition and artificial intelligence |
内容类型 | 其他 |
源URL | [http://hdl.handle.net/20.500.11897/398278] ![]() |
专题 | 数学科学学院 |
推荐引用方式 GB/T 7714 | Sun, Xichen,Cheng, Qiansheng,Feng, Jufu. From Penalized Maximum Likelihood to cluster analysis: A unified probabilistic framework of clustering. 2007-01-01. |
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