A Fast Clustering Method for Large Data Sets

Omar Kettani

Abstract


In this paper a deterministic clustering method based on the Katsavounidis, Kuo & Zhang (KKZ) seed procedure, is proposed. This approach has a lower computational complexity than the prominent k-means algorithm. We compared our method with a related deterministic clustering method: KKZ_ k-means (k-means initialized by KKZ). Performance evaluation demonstrates its effectiveness in term of average Silhouette index in various benchmark datasets.

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References


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