Assessing a mixture model for clustering with the integrated completed likelihood
Assessing a mixture model for clustering with the integrated completed likelihood
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DOI：
10.1109/34.865189
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发表时间：
2000-07-01
影响因子：
23.6
通讯作者：
Govaert, G
中科院分区：
文献类型：
--
作者：
Biernacki, C;Celeux, G;Govaert, G
我们建议在聚类分析环境中使用集成的完全似然来评估混合模型。为此，使用最大后验概率算子将观测数据分配给未知簇。然后，使用贝叶斯信息准则(BIC)来近似积分完全似然(ICL)。对ICL准则的模拟和真实数据的数值实验表明，该准则在选择混合模型和相应的簇数方面都表现出了良好的性能。尤其是。对于某些混合模型假设的违反，ICL似乎比BIC更稳健，并且它可以选择一些导致数据合理划分的簇。
We propose assessing a mixture model in a cluster analysis setting with the integrated completed likelihood. With this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the Integrated Completed Likelihood (ICL) is approximated using an a` la Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular. ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of clusters leading to a sensible partitioning of the data.