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Erschienen in:
2000 | OriginalPaper | Buchkapitel
Extended K-means with an Efficient Estimation of the Number of Clusters
verfasst von : Tsunenori Ishioka
Erschienen in: Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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We present a non-hierarchal clustering algorithm that can determine the optimal number of clusters by using iterations of k-means and a stopping rule based on BIC. The procedure requires twice the computation of k-means. However, with no prior information about the number of clusters, our method is able to get the optimal clusters based on information theory instead of on a heuristic method.