FROM PENALIZED MAXIMUM LIKELIHOOD TO CLUSTER ANALYSIS: A UNIFIED PROBABILISTIC FRAMEWORK OF CLUSTERING
Xichen Sun, Qiansheng Cheng, Jufu Feng · International Journal of Pattern Recognition and Artificial Intelligence · 2007
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.