A Survey on Cluster Based Outlier Detection Techniques in Data Stream

Abstract

In recent days, Data Mining (DM) is an emerging area of computational intelligence that provides new techniques, algorithms and tools for processing large volumes of data. Clustering is the most popular data mining technique today. Clustering used to separate a dataset into groups that finds intra-group similarity and inter-group similarity. Outlier detection (Anomaly) is to find small groups of data objects that are different when compared with rest of data. The outlier detection is an essential part of mining in data stream. Data Stream (DS) used to mine continuous arrival of high speed data Items. It plays an important role in the fields of telecommunication services, E-Commerce, Tracking customer behaviors and Medical analysis. Detecting outliers over data stream is an active research area. This survey presents the overview of fundamental outlier detection approaches and various types of outlier detection methods in data stream.

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

“…2: Fig. 2 General framework of anomaly detection [37] 1) Data: The data preparation phase where appropriate datasets are selected for anomaly detection. In this case, both multivariate and high-dimensional are considered.…”