Stability Region Based Expectation Maximization for Model-based Clustering

In spite of the initialization problem, the ExpectationMaximization (EM) algorithm is widely used for estimating the parameters in several data mining related tasks. Most popular model-based clustering techniques might yield poor clusters if the parameters are not initialized properly. To reduce the sensitivity of initial points, a novel algorithm for learning mixture models from multivariate data is introduced in this paper. The proposed algorithm takes advantage of TRUST-TECH (TRansformation Under STabilityreTaining Equilibra CHaracterization) to compute neighborhood local maxima on likelihood surface using stability regions. Basically, our method coalesces the advantages of the traditional EM with that of the dynamic and geometric characteristics of the stability regions of the corresponding nonlinear dynamical system of the log-likelihood function. Two phases namely, the EM phase and the stability region phase, are repeated alternatively in the parameter space to achieve improveme...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratnam
• Keywords: Algorithm, Data Mining, ICDM 2006, Mixture Model, Stability Region