International Conference on Computer and Knowledge Engineering

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Data Clustering using Chimp Optimization Algorithm

Authors :

SAYED PEDRAM HAERI BOROUJENI¹ ELNAZ PASHAEI²

1- Istanbul Aydin university 2- Istanbul Aydin university

Keywords :

(Meta-heuristic, Chimp Optimization Algorithm, Data clustering, Optimization problem)

Abstract :

Over the past few decades, many successful meta-heuristic algorithms have been widely used in solving data clustering problems due to their powerful capabilities and application effects. The Chimp Optimization Algorithm (ChOA) is a recently proposed meta-heuristic search algorithm that is inspired by chimps' individual intelligence and sexual motivation in their group hunting. ChOA has been proven to outperform other swarm intelligence algorithms in a variety of optimization problems. This study proposes a new approach based on ChOA for data clustering. ChOA, like other Swarm Intelligence-based Algorithms, starts with a population of candidate solutions and then calculates an objective function for them. In data clustering problems the population of candidate solutions is the position vectors of the centroids, and the objective function is the sum of intra-cluster distances between each sample to its nearest centroid. Following that, ChOA attempts to optimize the population by finding the best position vectors of optimal centroids. There are four different kinds of chimps in this regard: driver, chaser, barrier, and attacker. Furthermore, four major hunting moves including driving, blocking, chasing, and attacking are considered to search for the best solution. In this research, four datasets of the UCI machine learning repository are used to evaluate the performance of the proposed new approach. Experimental results illustrate that the proposed work significantly outperforms existing meta-heuristic methods regarding the value of the objective function, Error Rate (ER), and some other statistical tests.