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Grey Wolf Algorithm-Based Clustering Technique
| Content Provider | Scilit |
|---|---|
| Author | Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh |
| Copyright Year | 2017 |
| Abstract | The main problem of classical clustering technique is that it is easily trapped in the local optima. An attempt has been made to solve this problem by proposing the grey wolf algorithm (GWA)-based clustering technique, called GWA clustering (GWAC), through this paper. The search capability of GWA is used to search the optimal cluster centers in the given feature space. The agent representation is used to encode the centers of clusters. The proposed GWAC technique is tested on both artificial and real-life data sets and compared to six well-known metaheuristic-based clustering techniques. The computational results are encouraging and demonstrate that GWAC provides better values in terms of precision, recall, G-measure, and intracluster distances. GWAC is further applied for gene expression data set and its performance is compared to other techniques. Experimental results reveal the efficiency of the GWAC over other techniques. |
| Related Links | http://www.degruyter.com/downloadpdf/j/jisys.2017.26.issue-1/jisys-2014-0137/jisys-2014-0137.xml |
| Ending Page | 168 |
| Page Count | 16 |
| Starting Page | 153 |
| ISSN | 03341860 |
| e-ISSN | 2191026X |
| DOI | 10.1515/jisys-2014-0137 |
| Journal | Journal of Intelligent Systems |
| Issue Number | 1 |
| Volume Number | 26 |
| Language | English |
| Publisher | Walter de Gruyter GmbH |
| Publisher Date | 2017-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal of Intelligent Systems Cybernetical Science Industrial Engineering Grey Wolf Algorithm Data Clustering K-means Metaheuristics Journal: Journal of Intelligent Systems, Vol- 26 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Information Systems Software |
