QQ客服 官方微博 反馈留言

	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.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。