Data mining is a field at the intersection of computer science and statistics, is the process that involves, introducing patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure. In data mining K-means clustering algorithm is one of the efficient unsupervised learning algorithms to solve the well-known clustering problems. This method works for both the cases i.e. for known number of clusters in advance as well as unknown number of clusters. The user has the flexibility either to fix the number of clusters or input the minimum number of clusters required. But in this also it takes more computational time than the K-means for larger data sets. To overcome this problem we enhancing new K-means algorithm called Prototype based K-Means that can automatically find the cluster center with less number of time. A new method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity. The proposed algorithm has the more accuracy with less computational time comparatively original k-means clustering algorithm.
