A K-means-like Algorithm for K-medoids Clustering
نویسندگان
چکیده
Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. We evaluate the proposed algorithm using real and artificial data and compare with the results of other algorithms. The proposed algorithm takes the reduced time in computation with comparable performance as compared to the Partitioning Around Medoids.
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A simple and fast algorithm for k medoids clustering pdf
This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting.This paper proposes a new algorithm for K-medoids clustering which runs like the. A new Kmedoids clustering method that should be fast and efficient.
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