Outlier Detection Using Distributed Mining Technology In Large Database

International Journal of Computer Science and Engineering

© 2015 by SSRG - IJCSE Journal

Volume 2 Issue 2

Year of Publication : 2015

Authors : Mr.B.Muruganantham, Ms. Ankita Dubey

10.14445/23488387/IJCSE-V2I2P102

How to Cite?

Mr.B.Muruganantham, Ms. Ankita Dubey, "Outlier Detection Using Distributed Mining Technology In Large Database," SSRG International Journal of Computer Science and Engineering , vol. 2,  no. 2, pp. 6-11 , 2015. Crossref, https://doi.org/10.14445/23488387/IJCSE-V2I2P102

Abstract:

In many data analysis tasks, a large number of variables are being recorded or sampled. One of the first steps towards obtaining a coherent analysis is the detection of outlaying observations. A distributed approach is presented to detect distance-based outliers, based on the concept of outlier detection solving set. Data objects, which are different from or inconsistent with the remaining set of data, are called outliers. Many data mining techniques exists that attempt to find patterns that occur frequently in the data in which outliers are treated as noise that needs to be removed from a dataset. It is worth to notice that this is a unique peculiarity of distributed data mining technique, since other distributed methods for outlier detection are not able to return a model of the data that can be used for predicting novel outliers. In this paper, we analyze and detect the distance-based outliers in the data set. Priority is given to reduce the processing time of the supervisor node. This is done by simultaneous data computation by local nodes. It also deals with the drawbacks of the centralized system such as complete failure of the system and security.

Keywords:

Clustering, Distance-based outlier,Data mining, Outlier detection.

References:

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