Abstract
With the explosive increase of multimedia objects represented as high-dimensional vectors, clustering techniques for these objects have received much attention in recent years. However, clustering methods usually require a large amount of computational cost when calculating the distances between these objects. In this paper, for accelerating the greedy K-medoids clustering algorithm with \(L_1\) distance, we propose a new method consisting of the fast first medoid selection, lazy evaluation, and pivot pruning techniques, where the efficiency of the pivot construction is enhanced by our new pivot generation method called PGM2. In our experiments using real image datasets where each object is represented as a high-dimensional vector and \(L_1\) distance is recommended as their dissimilarity, we show that our proposed method achieved a reasonably high acceleration performance.
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Acknowledgments
This work was supported by JSPS Grant-in-Aid for Scientific Research (No. 16K16154).
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Fushimi, T., Saito, K., Ikeda, T., Kazama, K. (2017). Accelerating Greedy K-Medoids Clustering Algorithm with \(L_1\) Distance by Pivot Generation. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_9
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DOI: https://doi.org/10.1007/978-3-319-60438-1_9
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