An Efficient Social Spider Optimization for Data Clustering using Data Vector Representation

Abstract

In this article, we propose a new clustering algorithm namely an efficient social spider optimization for data clustering using data vector representation (ESSODCDI). It uses a data vector representation for each spider so that its memory requirements can be reduced. Unlike other nature-inspired algorithms, it requires lesser memory requirements. We find that its clustering results are by far better than those of other nature-inspired algorithms.