Enhanced Clustering Algorithm for Classification of Datasets

Clustering is one of the most important research areas in the field of data mining. In simple words, clustering is a division of data into different groups. Data are grouped into clusters in such a way that data of the same group are similar and those in other groups are dissimilar. It aims to minimize intra-class similarity while to maximize interclass dissimilarity. Clustering is an unsupervised learning technique. Clustering is useful to obtain interesting patterns and structures from a large set of data. Clustering can be applied in many areas, such as marketing studies, DNA analyses, city planning, text mining, and web documents classification. Large datasets with many attributes make the task of clustering complex. Many methods have been developed to deal with these problems. Here we have represented, partitioning based method – k-means, and enhanced clustering algorithm are implemented which will overcome the limitation of k-means and comparison is done between k-means and enhanced clustering algorithm applied on large datasets. The study given here explores the behaviour of this clustering algorithm with low processing cost and efficient result and less time complexity