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.
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Acknowledgement
The authors really appreciate the handling associate editor and all innominate reviews for their valuable comments. This work is supported by the National Natural Science Foundation of China (No. 62076042, No. 61572086), the Key Research and Development Project of Sichuan Province (No. 2020YFG0307, No. 2018TJPT0012), the Key Research and Development Project of Chengdu (No. 2019-YF05–02028-GX), the Innovation Team of Quantum Security Communication of Sichuan Province (No. 17TD0009), Sichuan Science and Technology Program under Grants 2018RZ0072, 20ZDYF0660, the Foundation of Chengdu University of Information Technology under Grant J201707, the National Key R&D Program of China under Grant (No. 2017YFB0802300), the Key Research and Development Project of Chengdu (No. 2019-YF05–02028-GX).
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Peng, Q., Zhang, S., Zhang, J., Huang, Y., Yao, B., Tang, H. (2021). Ball K-Medoids: Faster and Exacter. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_16
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