- 期刊
- OpenAccess
On Choosing a Mixture Model for Clustering
並列摘要
Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classification algorithm based on the classification likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AIC- respectively BIC-type approximations are applied. The resulting criteria turn out to be the sum of the AIC or BIC relative to each cluster plus an entropy term. The performance of our methods is evaluated by Monte-Carlo methods and on a real data set, showing in particular that the iterative estimation algorithm converges quickly in general, and thus the computational load is rather low.
延伸閱讀
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- Lin, C. Y. (2014). Fuzzy Clustering Algorithms on Mixture Model of Distributions [doctoral dissertation, Chung Yuan Christian University]. Airiti Library. https://doi.org/10.6840/cycu201400016