Fast Dimension-based Partitioning and Merging clustering algorithm |
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| Affiliation: | 1. Department of Information Technology, Faculty of Computers and Information, Menofiya University, Shebin El Kom, Menofiya, Egypt;2. Department of Computer Systems, Faculty of Computers and Information, Ain Shams University, Cairo, Egypt;3. Department of Information Systems, Faculty of Computers and Information, Menofiya University, Shebin El Kom, Menofiya, Egypt;1. Department of Information Management, College of Management, Chang Gung University, 259, Wen-Hwa 1st Road, Kwei-Shan, Taoyuan 333, Taiwan;2. Department of Industrial and Business Management, Graduate Institute of Business and Management, College of Management, Chang Gung University, 259, Wen-Hwa 1st Road, Kwei-Shan, Taoyuan 333, Taiwan;1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;2. School of Petroleum Engineering, Changzhou University, Changzhou 213164, China;1. Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India;2. School of Computer Engineering, Nanyang Technological University, Singapore;1. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan;2. Department of Business Administration, Shih Hsin University, Taipei 106, Taiwan |
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| Abstract: | Clustering multi-dense large scale high dimensional numeric datasets is a challenging task duo to high time complexity of most clustering algorithms. Nowadays, data collection tools produce a large amount of data. So, fast algorithms are vital requirement for clustering such data. In this paper, a fast clustering algorithm, called Dimension-based Partitioning and Merging (DPM), is proposed. In DPM, first, data is partitioned into small dense volumes during the successive processing of dataset dimensions. Then, noise is filtered out using dimensional densities of the generated partitions. Finally, merging process is invoked to construct clusters based on partition boundary data samples. DPM algorithm automatically detects the number of data clusters based on three insensitive tuning parameters which decrease the burden of its usage. Performance evaluation of the proposed algorithm using different datasets shows its fastness and accuracy compared to other clustering competitors. |
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| Keywords: | Clustering Subspace clustering Density-based clustering |
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