An Improved SSPCO Optimization Algorithm for Solve of the Clustering Problem

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a wide range of problems, such as data mining, data analysis, pattern recognition, and image segmentation. Clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. The aim of clustering is to collect data points. SSPCO optimization algorithm is a 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 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, an improved chaotic SSPCO algorithm is utilized for clustering data on different benchmarks and datasets; moreover, clustering with artificial bee colony algorithm and particle mass 9 clustering technique is compared. Clustering tests on 13 datasets from UCI machine learning repository have been done. The results show that clustering SSPCO algorithm is a clustering technique which is very efficient in clustering multivariate data.

Keywords:

Clustering , Neighbor , SSPCO , Clustering Error , algorithm