AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX
Clustering plays an important role in data mining and is applied widely in fields of pattern recognition, computer vision, and fuzzy control. In this paper, we proposed an improved clustering algorithm combined of both fuzzy k-means using weight Entropy and Calinski-Harabasz index. The advantage of...
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| Format: | Article |
| Language: | English |
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Dalat University
2018-07-01
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| Series: | Tạp chí Khoa học Đại học Đà Lạt |
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| Online Access: | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/408 |
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| _version_ | 1832573279462752256 |
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| author | Nguyễn Như Đồng Phan Thành Huấn |
| author_facet | Nguyễn Như Đồng Phan Thành Huấn |
| author_sort | Nguyễn Như Đồng |
| collection | DOAJ |
| description | Clustering plays an important role in data mining and is applied widely in fields of pattern recognition, computer vision, and fuzzy control. In this paper, we proposed an improved clustering algorithm combined of both fuzzy k-means using weight Entropy and Calinski-Harabasz index. The advantage of this method is that it does not only create efficient clustering but also has the ability to measure clusters and rate clusters to find the optimal number of clusters for practical needs. Finally, we presented experimental results on real-life datasets, which showed that the improved algorithm has the accuracy and efficiency of the existing algorithms. |
| format | Article |
| id | doaj-art-3aaed1106ce44b1cbcb3b21ab513a3f0 |
| institution | Kabale University |
| issn | 0866-787X 0866-787X |
| language | English |
| publishDate | 2018-07-01 |
| publisher | Dalat University |
| record_format | Article |
| series | Tạp chí Khoa học Đại học Đà Lạt |
| spelling | doaj-art-3aaed1106ce44b1cbcb3b21ab513a3f02025-02-02T05:22:22ZengDalat UniversityTạp chí Khoa học Đại học Đà Lạt0866-787X0866-787X2018-07-0182132310.37569/DalatUniversity.8.2.408(2018)236AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEXNguyễn Như Đồng0Phan Thành Huấn1Phòng Đào tạo, Trường Cao đẳng Kỹ nghệ II TP. Hồ Chí MinhBộ môn Tin học, Trường Đại học Khoa học Xã hội và Nhân văn, Đại học Quốc gia Tp.HCMClustering plays an important role in data mining and is applied widely in fields of pattern recognition, computer vision, and fuzzy control. In this paper, we proposed an improved clustering algorithm combined of both fuzzy k-means using weight Entropy and Calinski-Harabasz index. The advantage of this method is that it does not only create efficient clustering but also has the ability to measure clusters and rate clusters to find the optimal number of clusters for practical needs. Finally, we presented experimental results on real-life datasets, which showed that the improved algorithm has the accuracy and efficiency of the existing algorithms.http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/408chỉ số calinski-harabaszphân cụm mờtrọng số entropy. |
| spellingShingle | Nguyễn Như Đồng Phan Thành Huấn AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX Tạp chí Khoa học Đại học Đà Lạt chỉ số calinski-harabasz phân cụm mờ trọng số entropy. |
| title | AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX |
| title_full | AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX |
| title_fullStr | AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX |
| title_full_unstemmed | AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX |
| title_short | AN IMPROVED FUZZY K-MEANS CLUSTERING ALGORITHM BASED ON WEIGHT ENTROPY MEASUREMENT AND CALINSKI-HARABASZ INDEX |
| title_sort | improved fuzzy k means clustering algorithm based on weight entropy measurement and calinski harabasz index |
| topic | chỉ số calinski-harabasz phân cụm mờ trọng số entropy. |
| url | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/408 |
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