Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation
International Conference on Advances in Pattern RecognitionPublished 1 January 2000
Sid Ray, Rose H Turi
Citations544
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TL;DR
This paper presents a simple validity measure based on the intra-clusters and inter-cluster distance measures which allows the number of clusters to be determined automatically and is tested for synthetic images for which theNumber of clusters in known, and is also implemented for natural images.
Abstract
The main disadvantage of the k-means algorithm is that the number of clusters, K, must be supplied as a parameter. In this paper we present a simple validity measure based on the intra-cluster and inter-cluster distance measures which allows the number of clusters to be determined automatically. The basic procedure involves producing all the segmented images for 2 clusters up to Kmax clusters, where Kmax represents an upper limit on the number of clusters. Then our validity measure is calculated to determine which is the best clustering by finding the minimum value for our measure. The validity measure is tested for synthetic images for which the number of clusters in known, and is also implemented for natural images.
Keywords
Computer Science
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