A pillar algorithm for K-means optimization by distance maximization for initial centroid designation

Clustering performance of the K-means greatly relies upon the correctness of the initial centroids. Usually the initial centroids for the K-means clustering are determined randomly so that the determined centroids may reach the nearest local minima, not the global optimum. This paper proposes a new...

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Bibliographische Detailangaben

Hauptverfasser:	Barakbah, A.R., Kiyoki, Y.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithm design and analysis Biology Clustering algorithms Data mining Design optimization Image classification Information retrieval Iterative algorithms Partitioning algorithms Pattern recognition
Online-Zugang:	Volltext bestellen
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