A social spider optimization approach for clustering text documents

Recently, as clustering problem can be mapped to optimization problem, evolutionary optimization techniques have been used by researchers to improve accuracy and efficiency. Evolutionary techniques are stochastic general purpose methods for solving optimization problems. Swarm Intelligence is one such technique that deals with aggregative behavior of swarms and their complex interactions without any supervision. But, because of its robustness, Swarm intelligence paradigm seems to be even more attractive. We proposed a swarm intelligence algorithm called social spider optimization for text document clustering. This algorithm uses cooperative intelligent behavior of social spiders. Depending on its gender, each spider tends to reproduce a specialized behavior. It also helpsin reducing premature convergence and local minima problems considerably in text document clustering. It is compared with K-means clustering technique and found to give better results.