Solving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one of the latest meta-heuristic algorithms, which has a simple structure and it is easy to implement. The purpose of Chaos embedded Cat Swarm Optimization (CCSO) algorithm is to replace random values by chaotic ones to offer a stable algorithm that can allow for reaching the global optima to a large extent and improve the algorithm’s convergence speed. The proposed algorithm has been compared to other heuristic algorithms on standard data sets from UCI repository, and the experimental results demonstrate that the proposed algorithm yields high performance for solving the data clustering problem.Keywords: Data clustering, K-means, Cat Swarm Optimization, Chaos theory.

Solving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization Keywords:

Data clustering , K-means , Cat Swarm Optimization , Chaos theory