An Efficient Clustering Method in Unlabeled Data Sets Using KMBA Algorithm

Cluster analysis is one of the primary data analysis methods and K-means algorithm is well known for its efficiency in clustering large data sets. The K-means (KM) algorithm is one of the popular unsupervised learning clustering algorithms for cluster the large datasets but it is sensitive to the selection of initial cluster centroid, and selection of K value is an issue also sometimes it is hard to predict before the number of clusters that would be there in data. There are inefficient and universal methods for the selection of K value, till now we selected that as random value. In this paper, we propose a new metaheuristic method KMBA, the KM and Bat Algorithm (BA) based on the echolocation behavior of bats to identify the initial values for overcome the KM issues. The algorithm does not require the user to give in advance the number of clusters and cluster centre, it resolves the K-means (KM) cluster problem. This method finds the cluster centre which is generated by using the BA, and then it forms the cluster by using the KM. The combination of both KM and BA provides an efficient clustering and achieves higher efficiency. These clusters are formed by the minimal computational resources and time. The experimental result shows that proposed algorithm is better than the existing algorithms.