Survey on Outlier Detection in Data Mining

Abstract

Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. This paper has surveyed upon data mining and its various techniques that are used to extract useful information such as clustering, and has also surveyed the techniques that are used to detect the outliers. This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user.

RETRACTED: Survey of Clustering and Outlier Detection Techniques in Data Mining: A Research Perspective

“…The data objects that do not fit into the density of the cluster are acknowledged as the outlier. Markus M. Breunig et al has proposed a method in which outlier is find on the bases of the local outlier factor that how much the object is dissimilar from the other data objects with respect to the surrounding neighborhood [10,22]. Raghuvira Pratap et al have used a method based on density in which an efficient density based k-medoids clustering algorithm has been used to overcome the drawbacks of DBSCAN and k-medoids clustering algorithms [11] [16,25].…”

“…Thus, the outlierness metric of a data point is virtual in the sense that it is normally a ratio of density of this point against the the averaged densities of its nearest neighbors. Density-based methods feature a stronger modeling capability of outliers but require more expensive computation at the same time [22,28].…”

Index Based Hidden Outlier Detection in Metric Space

“…As a matter of necessity, the wide use of outlier detection has aroused enthusiasm in many researchers. Over the past few decades, many outlier definitions and detection algorithms have been proposed in the literature [4][5][6]. Among these, statistics-based outlier detection [7,8], which initiated a new era for data mining, has attracted an intensive study.…”

“…According to his definition, an object O in a dataset T is DB(𝑝, 𝐷) outlier if at least fraction p of the objects in T lies greater than distance D from O [10]. Soon afterwards, a number of distance-based definitions had been presented [4,6]. Based on these definitions and their relevant detection methods, many researches significantly improved both outlier detection accuracy and speed.…”

A Survey of Outlier Detection in Data Mining

“…In which most of data point are lies in t wo regions N1 and N2, and point that are far away fro m these region such as O1, O2, and data point in regions O3 are consider as Out lier [4] . Authors in [3] discuss different algorith m proposed by different researchers for detecting outliers.…”