Data clustering using cuckoo search algorithm (CSA)

Abstract

Cluster Analysis is a popular data analysis in data mining technique. Clusters play a vital role for users to organize, summarize and navigate the data effectively. Swarm Intelligence (SI) is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives. SI technique is integrated with clustering algorithms. This paper proposes new approaches for using Cuckoo Search Algorithm (CSA) to cluster data. It is shown how CSA can be used to find the optimally clustering N object into K clusters. The CSA is tested on various data sets, and its performance is compared with those of K-Means, Fuzzy C-Means, Fuzzy PSO and Genetic K-Means clustering. The simulation results show that the new method carries out better results than the K-Means, Fuzzy C-Means, Fuzzy PSO and Genetic K-Means.