Review: Outlier Detection using Clustering Based Techniques in Data Stream

Outlier detection is a critical data mining task used to detect and, where necessary, delete abnormal occurrences from dataset or tries to uncover useful anomalous and uneven patterns contained in large datasets. Various approaches are used to assign data points in different clusters based on different parameters. In this paper, we present a review of various existing approaches based on clustering of data points for detecting outliers from data set. The survey will cover the traditional outlier detection methods for static datasets, low dimensional datasets and recent developments that deal with outlier detection problems for dynamic data stream and high-dimensional datasets. In a comparative analysis, we highlight their benefits and drawbacks.