An Analysis of Outlier Detection through clustering method

Abstract

This research paper deals with an outlier which is known as an unusual behavior of any substance present in the spot. This is a detection process that can be employed for both anomaly detection and abnormal observation. This can be obtained through other members who belong to that data set. The deviation present in the outlier process can be attained by measuring certain terms like range, size, activity, etc. By detecting outlier one can easily reject the negativity present in the field. For instance, in healthcare, the health condition of a person can be determined through his latest health report or his regular activity. When found the person being inactive there may be a chance for that person to be sick. Many approaches have been used in this research paper for detecting outliers. The approaches used in this research are 1) Centroid based approach based on K-Means and Hierarchical Clustering algorithm and 2) through Clustering based approach. This approach may help in detecting outlier by grouping all similar elements in the same group. For grouping, the elements clustering method paves a way for it. This research paper will be based on the above mentioned 2 approaches.