A cultural algorithm for data clustering
Mohammadreza Shahriari · International journal of industrial mathematics. · 2015
Clustering is a widespread data analysis and data mining technique in many elds of study such as engineering, medicine, biology and the like. The aim of clustering is to collect data points. In this paper, a Cultural Algorithm (CA) is presented to optimize partition with N objects into K clusters. The CA is one of the eective methods for searching into the problem space in order to nd a near optimal solution. This algorithm has been tested on dierent scale datasets and has been compared with other well-known algorithms in clustering, such as K-means, Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) algorithm. The results illustrate that the proposed algorithm has a good prociency in obtaining the desired results.