Abstract
K-medoids clustering is a popular partition-based clustering technique to identify usual patterns in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for it. In this paper, we propose an efficient K-medoids clustering algorithm which preserves the clustering performance by following the notion of a simple and fast K-medoids algorithm while improving the computational efficiency. The proposed algorithm does not require pre-calculating the distance matrix and therefore is applicable to large scale datasets. When a simple pruning rule is used, it can give near linear time performance. To this end, the complexity of this proposed algorithm is analyzed and found to be lower than that of the state of the art K-medoids algorithms. We test our algorithm on real data sets with millions of examples and experimental results show that the proposed algorithm outperforms state-of-the-art K-medoids clustering algorithms.
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Wang, X., Wang, X., Wilkes, D.M. (2020). An Efficient K-Medoids Clustering Algorithm for Large Scale Data. In: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment. Springer, Singapore. https://doi.org/10.1007/978-981-13-9217-7_5
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DOI: https://doi.org/10.1007/978-981-13-9217-7_5
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