Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
381709 | Engineering Applications of Artificial Intelligence | 2006 | 9 Pages |
Abstract
This paper presents an online algorithm for mixture model-based clustering. Mixture modeling is the problem of identifying and modeling components in a given set of data. The online algorithm is based on unsupervised learning of finite Dirichlet mixtures and a stochastic approach for estimates updating. For the selection of the number of clusters, we use the minimum message length (MML) approach. The proposed method is validated by synthetic data and by an application concerning the dynamic summarization of image databases.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Nizar Bouguila, Djemel Ziou,