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Clustering via the Bayesian Information Criterion with Applications in Speech
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shaobing, Scot T |
| Copyright Year | 2004 |
| Abstract | One difficult problem we are often faced with in clustering analysis is how to choose the number of clusters. In this paper, we propcse to choose the number of clusters by optimizing the Bq2yesian information criterion (BIC), a model selection criierion in the statistics literature. We develop a termination criterion for the hierarchical clustering methods which optimizes the BIC criterion in a greedy fashion. The resulting algorithms are fully automatic. Our experiments on Gaussian mixture modeling and speaker clustering demonstrate that the BIC criterion is able to choose the number of clusters according to the intrinsic complexity present in the data. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://sydney.edu.au/engineering/it/~comp5318/assignment/chenclustering.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Akaike information criterion Bayesian information criterion Cluster analysis Experiment Geographic Information Systems Greedy algorithm Hierarchical clustering Mixture model Model selection Normal Statistical Distribution statistical cluster |
| Content Type | Text |
| Resource Type | Article |
