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Selecting Optimal Weighted Medoids for Clustering
Abstract
Clustering is the process of grouping data into clusters. Partitioning is an important clustering method. K-medoids is a classical partitioning method. K-medoids generates k clusters for a dataset of n objects using distance measure. But this algorithm may not be suitable for many real life applications. In addition to distance measure, the medoid selection may depend on many other factors in real life. A new weighted k-medoids algorithm is proposed in this paper to find optimal medoids using a new measure, weights of the medoids.
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