Efficient K-Mean Clustering Algorithm for Large Datasets using Data Mining Standard Score Normalization

Abstract

In this paper, the clustering and data mining techniques has been introduced. The data mining is useful for extract the useful information from the large database/dataset. For extract the information with efficient factor, the data mining Normalization techniques can be used. These techniques are Min-Max, Z-Scaling and decimal Scaling normalization. Mining of data becomes essential thing for easy searching of data with normalization. This paper has been proposed the efficient K-Mean Clustering algorithm which generates the cluster in less time. Cluster Analysis seeks to identify homogeneous groups of objects based on the values of their attribute. The Z-Score normalization technique has been used with Clustering concept. The number of large records dataset has been generated and has been considered for analyze the results. The existing algorithm has been analyzed by WEKA Tool and proposed algorithm has been implemented in C#.net. The results have been analyzed by generating the timing comparison graphs and proposed works shows the efficiency in terms of time and calculation