Outlier Analysis Approaches in Data Mining

Outlier Analysis Approaches in Data Mining
Author(s):
Krishna Modi, Prof Bhavesh Oza
Keywords:
Data Mining, Outliers, Anomalies, Supervised
Abstract
Data Mining is used to the extract interesting patterns of the data from the datasets. Outlier detection is one of the important aspects of data mining to find out those objects that differ from the behavior of other objects. Finding outliers from a collection of patterns is a popular problem in the field of data mining. A key challenge with outlier detection is that it is not a well expressed problem like clustering so Outlier Detection as a branch of data mining requires more attention. Outlier Detection methods can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. Detecting outliers and analyzing large data sets can lead to discovery of hidden knowledge in area such as fraud detection, Terrorism Activities, telecommunication, web logs, and web document, etc. In this paper, we explained five types of outlier, different approaches to detect outliers, their advantages and disadvantages and applications.
Article Details
Unique Paper ID: 144122 Publication Volume & Issue: Volume 3, Issue 7 Page(s): 6 - 12
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