Combining K-means and Particle Swarm Optimization for Dynamic Data Clustering Problems
碩士 === 大同大學 === 資訊經營學系(所) === 96 === This paper presents a dynamic data clustering algorithm named K-means with Combinatorial Particle Swarm Optimization (KCPSO). Unlike the K-means method, KCPSO does not need a specific number of clusters before clustering is performed and is able to find the prope...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2008
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/92588567584481842868 |
| Summary: | 碩士 === 大同大學 === 資訊經營學系(所) === 96 === This paper presents a dynamic data clustering algorithm named K-means with Combinatorial Particle Swarm Optimization (KCPSO). Unlike the K-means method, KCPSO does not need a specific number of clusters before clustering is performed and is able to find the proper number of clusters automatically. A predefined parameter of maximum cluster number is given, and a cluster validity index is employed to evaluate the clustering results in order to adjust the cluster number of each particle. Then, the cluster center among particles is adjusted by using K-means. KCPSO is able not only to avoid the drawback of K-means but also to determine the proper number of cluster. KCPSO has been developed into a system and evaluated by testing some datasets. Results show that KCPSO is an effective algorithm in providing the optimal number of clusters.
|
|---|