Abstract: Cluster analysis can be viewed as a result of the natural evolution of the vast amount of data from daily life, and can discover invisible feature information to contribute to the analysis. K-means algorithm is one of the wide data clustering methods in a variety of real-world applications thanks to its simpleness. However, the k-means is sensitive to noise and outlier data points because a small number of such data can substantially influence the mean value of the cluster. In light of this, the k-medoids algorithm selects a point as a new center that minimizes the sum of the dissimilarities in the cluster, to diminish such sensitivity to outliers. Nevertheless, the line of the k-medoids algorithm is limited by its amounts of computation and not to handle with data efficiently. To this end, we present a novel k-medoids algorithm motivated by the theory of ball cluster, relationship between clusters and partitioning cluster for assigning samples into their nearest medoids efficiently, called ball k-medoids, which drop the distance calculation of sample-medoid significantly. Moreover, a threshold is inferenced by the rollback method for reducing computation of medoid-medoid distance and accelerating clustering. Experiments finally demonstrate that the performance of ball k-medoids achieves more efficient in comparison with other k-medoids algorithms, and it performs exacter accuracy compared with k-means.
Publication Year: 2021
Publication Date: 2021-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 1
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