Initialization method of finite mixture model using kernel density estimation and application on model-based clustering

2018 ◽  
Vol 29 (2) ◽  
pp. 327-337
Author(s):  
Hyun-ju Cho ◽  
Yeojin Chung ◽  
Youngmin Kim
Keyword(s):  
Density Estimation ◽  
Mixture Model ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Based Clustering ◽  
Model Based

scholarly journals Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging

2007 ◽  
Vol 26 (5) ◽  
pp. 696-711 ◽  
Author(s):  
J. Tohka ◽  
E. Krestyannikov ◽  
I.D. Dinov ◽  
A.M. Graham ◽  
D.W. Shattuck ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Based ◽  
Voxel Classification

scholarly journals Summarizing Finite Mixture Model with Overlapping Quantification

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1503
Author(s):  
Shunki Kyoya ◽  
Kenji Yamanishi
Keyword(s):  
Mutual Information ◽  
Mixture Models ◽  
Mixture Model ◽  
Finite Mixture Models ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Based Clustering ◽  
Multiple Components ◽  
Clustering Data ◽  
Novel Concept

Finite mixture models are widely used for modeling and clustering data. When they are used for clustering, they are often interpreted by regarding each component as one cluster. However, this assumption may be invalid when the components overlap. It leads to the issue of analyzing such overlaps to correctly understand the models. The primary purpose of this paper is to establish a theoretical framework for interpreting the overlapping mixture models by estimating how they overlap, using measures of information such as entropy and mutual information. This is achieved by merging components to regard multiple components as one cluster and summarizing the merging results. First, we propose three conditions that any merging criterion should satisfy. Then, we investigate whether several existing merging criteria satisfy the conditions and modify them to fulfill more conditions. Second, we propose a novel concept named clustering summarization to evaluate the merging results. In it, we can quantify how overlapped and biased the clusters are, using mutual information-based criteria. Using artificial and real datasets, we empirically demonstrate that our methods of modifying criteria and summarizing results are effective for understanding the cluster structures. We therefore give a new view of interpretability/explainability for model-based clustering.

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