Evaluation of the Selection of the Initial Seeds for K-Means Algorithm

Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Data Management
Format: PDF
Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-means algorithm is one of partitioning clustering methods and is adequate to cluster a lot of data rapidly and easily. The problem is it is too dependent on initial centers of clusters and needs the time of allocation and recalculation. The authors compare random method, max average distance method and triangle height method for selecting initial seeds in K-means algorithm. It reduces total clustering time by minimizing the number of allocation and recalculation.

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