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      Two-phase clustering algorithm with density exploring distance measure

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          Abstract

          Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (DED) measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

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          Most cited references 21

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          The global k-means clustering algorithm

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            Genetic algorithm-based clustering technique

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              Generalized picture distance measure and applications to picture fuzzy clustering

               Le Son (2016)
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                Author and article information

                Contributors
                Journal
                TRIT
                CAAI Transactions on Intelligence Technology
                CAAI Trans. Intell. Technol.
                The Institution of Engineering and Technology
                2468-2322
                March 2018
                5 March 2018
                29 March 2018
                : 3
                : 1
                : 59-64
                Affiliations
                Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University , PO Box 224, Xi'an 710071, People's Republic of China
                Article
                TRIT.2018.0006 CIT.2018.0006
                10.1049/trit.2018.0006

                This is an open access article published by the IET, Chinese Association for Artificial Intelligence and Chongqing University of Technology under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

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