A Fast Global Center Fuzzy Clustering Method
In terms of the problems that the fuzzy C-means algorithm is sensitive to the initial center, easy to fall into the local optimal solution, and the algorithm iteration speed is slow, a rapid global center fuzzy clustering system model is established according to the global center theory of fuzzy...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2019-08-01
|
| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1716 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849389358797291520 |
|---|---|
| author | SUN Dong-pu TAN Jie-qiong |
| author_facet | SUN Dong-pu TAN Jie-qiong |
| author_sort | SUN Dong-pu |
| collection | DOAJ |
| description |
In terms of the problems that the fuzzy C-means algorithm is sensitive to the initial center, easy to fall into the local optimal solution, and the algorithm iteration speed is slow, a rapid global center fuzzy clustering system model is established according to the global center theory of fuzzy clustering, and the relevant theoretical analysis and algorithm process is given. In the model, the initial centroid is determined by the DKC value scheme, and the self-defined optimization function is proposed based on the AM metric. According to this function, the cluster centers are dynamically added one by one to every stage of algorithm operation until the algorithm converges. Through experimental comparison and verification, the process reduces the influence of random selection of cluster centers on clustering results, and jumps out of local optimal solution, reduces computation, and has higher clustering accuracy and faster convergence speed. |
| format | Article |
| id | doaj-art-0de631bfb67245ebbef47c0f6169d6f4 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2019-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-0de631bfb67245ebbef47c0f6169d6f42025-08-20T03:41:59ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832019-08-01240411011710.15938/j.jhust.2019.04.019A Fast Global Center Fuzzy Clustering MethodSUN Dong-pu0TAN Jie-qiong1Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaDepartment of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China In terms of the problems that the fuzzy C-means algorithm is sensitive to the initial center, easy to fall into the local optimal solution, and the algorithm iteration speed is slow, a rapid global center fuzzy clustering system model is established according to the global center theory of fuzzy clustering, and the relevant theoretical analysis and algorithm process is given. In the model, the initial centroid is determined by the DKC value scheme, and the self-defined optimization function is proposed based on the AM metric. According to this function, the cluster centers are dynamically added one by one to every stage of algorithm operation until the algorithm converges. Through experimental comparison and verification, the process reduces the influence of random selection of cluster centers on clustering results, and jumps out of local optimal solution, reduces computation, and has higher clustering accuracy and faster convergence speed.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1716fuzzy clusteringglobal centerdkcam metricnoise point |
| spellingShingle | SUN Dong-pu TAN Jie-qiong A Fast Global Center Fuzzy Clustering Method Journal of Harbin University of Science and Technology fuzzy clustering global center dkc am metric noise point |
| title | A Fast Global Center Fuzzy Clustering Method |
| title_full | A Fast Global Center Fuzzy Clustering Method |
| title_fullStr | A Fast Global Center Fuzzy Clustering Method |
| title_full_unstemmed | A Fast Global Center Fuzzy Clustering Method |
| title_short | A Fast Global Center Fuzzy Clustering Method |
| title_sort | fast global center fuzzy clustering method |
| topic | fuzzy clustering global center dkc am metric noise point |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1716 |
| work_keys_str_mv | AT sundongpu afastglobalcenterfuzzyclusteringmethod AT tanjieqiong afastglobalcenterfuzzyclusteringmethod AT sundongpu fastglobalcenterfuzzyclusteringmethod AT tanjieqiong fastglobalcenterfuzzyclusteringmethod |