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Detection of Outliers in Large Dataset using Distributed Approach

Publication Date : 31/12/2014

Author(s) :

Jyoti Narayan Shinde.

Volume/Issue :
Volume 1
Issue 6
(12 - 2014)

Abstract :

In this paper, a distributed method is introduced for detecting distance-based outliers in very large data sets. The approach is based on the concept of outlier detection solving set, which is a small subset of the data set that can be also employed for predicting novel outliers. The method exploits parallel computation in order to obtain vast time savings. Indeed, beyond preserving the correctness of the result, the proposed schema exhibits excellent performances. From the theoretical point of view, for common settings, the temporal cost of our algorithm is expected to be at least three orders of magnitude faster than the classical nested-loop like approach to detect outliers. Experimental results show that the algorithm is efficient and that it’s running time scales quite well for an increasing number of nodes. We discuss also a variant of the basic strategy which reduces the amount of data to be transferred in order to improve both the communication cost and the overall runtime. Importantly, the solving set computed in a distributed environment has the same quality as that produced by the corresponding centralized method.

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Detection of Outliers in Large Dataset using Distributed Approach

December 30, 2014