Name
Abstract | ||
|---|---|---|
In this paper, a novel Discrete Particle Swarm Clustering algorithm (DPSC) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form and an efficient approach is developed to move the particles for constructing new clustering solutions. DPSC 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 result obtained by the proposed algorithm has been compared with the published results of Combinatorial Particle Swarm Optimization (CPSO) algorithm and Genetic Algorithm (GA). The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1517303.1517321 | Bangalore Compute Conf. |
Keywords | Field | DocType |
performance analysis,novel discrete particle swarm,combinatorial particle swarm optimization,clustering algorithm,dpsc algorithm,new clustering solution,data clustering,partitional data,proposed algorithm,performance metrics,particle swarm,genetic algorithm,particle swarm optimization | k-medians clustering,Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Correlation clustering,Computer science,Algorithm,Determining the number of clusters in a data set,Multi-swarm optimization,Cluster analysis,k-medoids | Conference |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Karthi | 1 | 3 | 1.06 |
| S. Arumugam | 2 | 16 | 2.07 |
| K. Rameshkumar | 3 | 44 | 4.19 |