Hybrid Clustering Algorithm with Modifications Enhanced K- Means and Hierarchal Clustering

Clustering is an essential task in Data Mining process which is used for the purpose to make groups or clusters of the given data set based on the similarity between them. K-Means clustering is a clustering method in which the given data set is divided into K number of clusters. This paper is intended to give the introduction about K-means clustering and its algorithm. The experimental result of K-means clustering and its performance in case of execution time is discussed here. But there are certain limitations in K-means clustering algorithm such as it takes more time for execution. So in order to reduce the execution, time we are using the Ranking Method. And also shown that how clustering is performed in less execution time as compared to the traditional method. This work makes an attempt at studying the feasibility of Kmeans clustering algorithm in data mining using the Ranking Method. Modifications in hard K-means algorithm such that algorithm can be used for clustering data with categorical attributes. to use the algorithm for categorical data modifications in distance and prototype calculation are proposed. To use the algorithm on numerical attribute values, means is calculated to represent centre, and Euclidean distance is used to calculate distance. Keywords— Clustering, Hierarchical Clustering, K-means, Ranking method, SOM