Self-Adaptive K-Means Based on a Covering Algorithm
The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial c...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/7698274 |
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| author | Yiwen Zhang Yuanyuan Zhou Xing Guo Jintao Wu Qiang He Xiao Liu Yun Yang |
| author_facet | Yiwen Zhang Yuanyuan Zhou Xing Guo Jintao Wu Qiang He Xiao Liu Yun Yang |
| author_sort | Yiwen Zhang |
| collection | DOAJ |
| description | The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions. |
| format | Article |
| id | doaj-art-bb474532500943828954fb035e1ba1e4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-bb474532500943828954fb035e1ba1e42025-02-03T06:12:36ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/76982747698274Self-Adaptive K-Means Based on a Covering AlgorithmYiwen Zhang0Yuanyuan Zhou1Xing Guo2Jintao Wu3Qiang He4Xiao Liu5Yun Yang6School of Computer Science and Technology, Anhui University, Hefei 230601, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaSchool of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaSchool of Information Technology, Deakin University, Melbourne, VIC 3125, AustraliaSchool of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaThe K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.http://dx.doi.org/10.1155/2018/7698274 |
| spellingShingle | Yiwen Zhang Yuanyuan Zhou Xing Guo Jintao Wu Qiang He Xiao Liu Yun Yang Self-Adaptive K-Means Based on a Covering Algorithm Complexity |
| title | Self-Adaptive K-Means Based on a Covering Algorithm |
| title_full | Self-Adaptive K-Means Based on a Covering Algorithm |
| title_fullStr | Self-Adaptive K-Means Based on a Covering Algorithm |
| title_full_unstemmed | Self-Adaptive K-Means Based on a Covering Algorithm |
| title_short | Self-Adaptive K-Means Based on a Covering Algorithm |
| title_sort | self adaptive k means based on a covering algorithm |
| url | http://dx.doi.org/10.1155/2018/7698274 |
| work_keys_str_mv | AT yiwenzhang selfadaptivekmeansbasedonacoveringalgorithm AT yuanyuanzhou selfadaptivekmeansbasedonacoveringalgorithm AT xingguo selfadaptivekmeansbasedonacoveringalgorithm AT jintaowu selfadaptivekmeansbasedonacoveringalgorithm AT qianghe selfadaptivekmeansbasedonacoveringalgorithm AT xiaoliu selfadaptivekmeansbasedonacoveringalgorithm AT yunyang selfadaptivekmeansbasedonacoveringalgorithm |