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A Density-based Clustering Method
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 Title & Authors
A Density-based Clustering Method
Ahn, Sung Mahn; Baik, Sung Wook;
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 Abstract
This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values
 Keywords
clustering method;closeness measure;Gaussian mixture model;maximum penalized likelihood;EM algorithm;
 Language
Korean
 Cited by
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