A Clustering Approach by SSPCO Optimization Algorithm Based on Chaotic Initial Population

Abstract

Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters' objects is the main task of clustering analysis. SSPCO optimization algorithm is a<br />new optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. One of the things that smart algorithms are applied to solve is the problem of clustering. Clustering is employed as a<br />powerful tool in many data mining applications, data analysis, and data compression in order to group data on the number of clusters (groups). In the present article, a chaotic SSPCO algorithm is utilized for clustering<br />data on different benchmarks and datasets; moreover, clustering with artificial bee colony algorithm and particle mass 9 clustering technique is compared. Clustering tests have been done on 13 datasets from UCI<br />machine learning repository. The results show that clustering SSPCO algorithm is a clustering technique which is very efficient in clustering multivariate data.