Extended K-means with an Efficient Estimation of the Number of Clusters

Authors:
Tsunenori Ishioka
Affiliations:
-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

Citing 4
Cited 2

On the number of clusters

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Accelerating exact k-means algorithms with geometric reasoning

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Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

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X-means: Extending K-means with Efficient Estimation of the Number of Clusters

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning

Dynamic k-means: a clustering technique for moving object trajectories

International Journal of Intelligent Information and Database Systems
Shortest-linkage-based parallel hierarchical clustering on main-belt moving objects of the solar system

Future Generation Computer Systems

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Abstract

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.