An effective implementation of Social Spider Optimization for text document clustering using single cluster approach

Since the last decade, researchers have been concentrating on high quality clustering methods for the effective analysis of the growing data. Algorithms inspired by nature have become part of a widely used class of computing methods, because of their ability to adjust to variety of conditions. These nature inspired algorithms have been frequently used for solving complex, real-world optimization problems. In recent years, they have emerged as powerful optimization techniques. These algorithms inspired by the cooperative behavior of social animals have the ability to provide better solutions for clustering problems. Social Spider Optimization (SSO) is a population based stochastic optimization algorithm. It has been used to solve many complicated optimization problems. It simulates the behavior of social spiders. In this paper, a new and more effective implementation of SSO (ESSOSC) for solving text document clustering problem is specified by using single cluster implementation for each spider.