Modified K-Means for Better Initial Cluster Centres
نویسنده
چکیده
The k-means clustering algorithm is most popularly used in data mining for real world applications. The efficiency and performance of the k-means algorithm is greatly affected by initial cluster centers as different initial cluster centers often lead to different clustering. In this paper, we propose a modified k-means algorithm which has additional steps for selecting better cluster centers. We compute Min and Max distance for every cluster and find high density objects for selection of better k. Key Terms: k-means; clustering; data mining; initial cluster centers; density objects
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تاریخ انتشار 2013