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- W2368604885 abstract "Traditional K-medoids clustering algorithm has some drawbacks,such as its clustering results being sensitive to initial cluster centers and its deficiency in large datasets.Although the fast K-medoids algorithm overcame the shortcomings of traditional K-medoids,it has the potential disadvantages of selecting the exemplars in the same cluster as initial seeds for different clusters.To overcome the shortcomings of the traditional K-medoids and the fast K-medoids clustering algorithms,a granular computing based K-medoids clustering algorithm was proposed in this paper.The algorithm defined a new similarity function between samples via pooling granularity,where the granules were produced via the equivalence relationship.The density of a granule was defined according to the number of samples in it,after that the K samples closest to the centers of the first K granules were selected as the initial centers for K-medoids clustering algorithm to cluster datasets.The experimental results on the datasets from UCI machine learning repository and on the synthetic datasets all demonstrate that the new granular computing based K-medoids clustering algorithm can find much better initial centers.Its clustering accuracy and its clustering error are better than those of the traditional K-medoids and the fast K-medoids clustering algorithms.It can get much more stable results and can be applied to cluster large datasets." @default.
- W2368604885 created "2016-06-24" @default.
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- W2368604885 date "2012-01-01" @default.
- W2368604885 modified "2023-09-24" @default.
- W2368604885 title "New K-medoids clustering algorithm based on granular computing" @default.
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