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.