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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Dalat University
2018-07-01
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| Series: | Tạp chí Khoa học Đại học Đà Lạt |
| Subjects: | |
| Online Access: | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/408 |
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