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An Efficient Clustering Method in Unlabeled Data Sets using KMBA Algorithm

G. Komarasamy, Amitabh Wahi


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.


Centroid, Clustering, Metaheuristic, BAT Algorithm.

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