INITIALIZATION K-MEANS USING ANT COLONY OPTIMIZATION

Clustering is a machine learning technique that places data elements into related groups. Clustering can be defined as a process of organizing objects into groups whose members are similar in some way. The primary goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. K-Means (KM) is one of the widely used algorithms in clustering techniques. KM is the simplest unsupervised learning algorithms that can solve the well-known clustering problem. Ant Colony Optimization (ACO) is one of the most popular evolutionary algorithms inspired from nature and utilized in the field of clustering. Thus ACO can be defined as the adaptive heuristic search algorithm premised on the evolutionary ideas of natural behavior of ants. ACO is used to initialize the KM clustering algorithm. This will help in better clustering result with lesser error rates. From the experimental results, it is observed that the usage of ACO results in better clustering result when compared to existing techniques (KM)