Name
Abstract | ||
|---|---|---|
In this paper, a novel Discrete Particle Swarm Optimization Algorithm (DPSOA) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form. The DPSOA algorithm uses of a simple probability approach to construct the velocity of particle followed by a search scheme to constructs the clustering solution. DPSOA algorithm has been applied to solve the data clustering problems by considering two performance metrics, such as TRace Within criteria (TRW) and Variance Ratio Criteria (VRC). The results obtained by the proposed algorithm have been compared with the published results of Basic PSO (B-PSO) algorithm, Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Combinatorial Particle Swarm Optimization (CPSO) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-03211-0_7 | Studies in Computational Intelligence |
Keywords | Field | DocType |
data clustering,differential evolution,genetic algorithm | Particle swarm optimization,Mathematical optimization,Derivative-free optimization,Computer science,Meta-optimization,Algorithm,Multi-swarm optimization,Differential evolution,Cluster analysis,Genetic algorithm,Metaheuristic | Conference |
Volume | ISSN | Citations |
236 | 1860-949X | 2 |
PageRank | References | Authors |
0.38 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Karthi | 1 | 3 | 1.06 |
| S. Arumugam | 2 | 16 | 2.07 |
| K. Rameshkumar | 3 | 44 | 4.19 |