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Mixture Complexity and Its Application to Gradual Clustering Change Detection
We consider measuring the number of clusters (cluster size) in the finite mixture models for interpreting their structures. Many existing information criteria have been applied for this issue by regarding it as the same as 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 argue that the cluster size should be measured as a continuous value and propose a new criterion called mixture complexity (MC) todoi:10.3390/e24101407 fatcat:qlwzhnw2wvchheudcd72vvdzji