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Mixture Complexity and Its Application to Gradual Clustering Change Detection
[article]
2020
arXiv
pre-print
In model-based clustering using finite mixture models, it is a significant challenge to determine the number of clusters (cluster size). It used to be equal to the number of mixture components (mixture size); however, this may not be valid in the presence of overlaps or weight biases. In this study, we propose to continuously measure the cluster size in a mixture model by a new concept called mixture complexity (MC). It is formally defined from the viewpoint of information theory and can be
arXiv:2007.07467v1
fatcat:esc7lk6sxfcmtj5if4wvxptgli