K-Means Cloning: Adaptive Spherical K-Means Clustering
We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Algorithms |
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| Online Access: | http://www.mdpi.com/1999-4893/11/10/151 |
| _version_ | 1818032450307096576 |
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| author | Abdel-Rahman Hedar Abdel-Monem M. Ibrahim Alaa E. Abdel-Hakim Adel A. Sewisy |
| author_facet | Abdel-Rahman Hedar Abdel-Monem M. Ibrahim Alaa E. Abdel-Hakim Adel A. Sewisy |
| author_sort | Abdel-Rahman Hedar |
| collection | DOAJ |
| description | We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm. |
| first_indexed | 2024-12-10T06:07:34Z |
| format | Article |
| id | doaj.art-682e9ebf7f064b74afe8e37f232254d5 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-10T06:07:34Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-682e9ebf7f064b74afe8e37f232254d52022-12-22T01:59:39ZengMDPI AGAlgorithms1999-48932018-10-01111015110.3390/a11100151a11100151K-Means Cloning: Adaptive Spherical K-Means ClusteringAbdel-Rahman Hedar0Abdel-Monem M. Ibrahim1Alaa E. Abdel-Hakim2Adel A. Sewisy3Department of Computer Science, Faculty of Comp. & Info, Assiut University, Assiut 71526, EgyptDepartment of Mathematics, Faculty of Science, Al-Azhar University, Assiut Branch, Assiut 71524, EgyptDepartment of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi ArabiaDepartment of Computer Science, Faculty of Comp. & Info, Assiut University, Assiut 71526, EgyptWe propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm.http://www.mdpi.com/1999-4893/11/10/151data miningclustering analysisadaptive K-meanssimulated annealing |
| spellingShingle | Abdel-Rahman Hedar Abdel-Monem M. Ibrahim Alaa E. Abdel-Hakim Adel A. Sewisy K-Means Cloning: Adaptive Spherical K-Means Clustering Algorithms data mining clustering analysis adaptive K-means simulated annealing |
| title | K-Means Cloning: Adaptive Spherical K-Means Clustering |
| title_full | K-Means Cloning: Adaptive Spherical K-Means Clustering |
| title_fullStr | K-Means Cloning: Adaptive Spherical K-Means Clustering |
| title_full_unstemmed | K-Means Cloning: Adaptive Spherical K-Means Clustering |
| title_short | K-Means Cloning: Adaptive Spherical K-Means Clustering |
| title_sort | k means cloning adaptive spherical k means clustering |
| topic | data mining clustering analysis adaptive K-means simulated annealing |
| url | http://www.mdpi.com/1999-4893/11/10/151 |
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