Exploiting the anomaly detection for high dimensional data using descriptive approach of data mining

Now a days the enormity of High Dimensional data has been used in various real life applications. Most of the data mining techniques in descriptive analysis require a part of data sets into a fixed number of clusters based on user input, explicitly or learning by observation. For High Dimensional data set these fixed number of cluster given by user are not good estimation, because it leads to inefficient data distribution or it leads to various outlier. An efficient and scalable data mining technique is requires to deal with such type of data. In this paper we present a new algorithm to approach the problem of outlier detection in High Dimensional data with the help of descriptive analysis. Our technique is hybridization of density-based outlier detection and distance-based outlier detection technique.