Procreant PSO for document clustering

Fast and high-quality document clustering algorithms play an important role towards the goal of organizing large amounts of information into a small number of meaningful clusters. Traditional clustering algorithms will search only a small sub-set of all possible clustering and consequently, there is no guarantee that the solution found will be optimal. This paper presents Procreant PSO (PPSO) algorithm for document clustering. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed to be local optimum. In this paper a modification strategy is proposed for the particle swarm optimization (PSO) algorithm. The strategy adds reproduction when the stagnation in movement of the particle is identified and carries out local search to improve the goodness of fitting. Procreation has the capability to achieve faster convergence and better solution. Experiments results are examined with document corpus. It demonstrates that the PPSO algorithm statistically outperforms the Simple PSO and K-Means.