Title

Fast Parallel Outlier Detection For Categorical Datasets Using Mapreduce

Abstract

Outlier detection has received considerable attention in many applications, such as detecting network attacks or credit card fraud. The massive datasets currently available for mining in some of these outlier detection applications require large parallel systems, and consequently parallelizable outlier detection methods. Most existing outlier detection methods assume that all of the attributes of a dataset are numerical, usually have a quadratic time complexity with respect to the number of points in the dataset, and quite often they require multiple dataset scans. In this paper, we propose a fast parallel outlier detection strategy based on the Attribute Value Frequency (AVF) approach, a high-speed, scalable outlier detection method for categorical data that is inherently easy to parallelize. Our proposed solution, MR-AVF, is based on the MapReduce paradigm for parallel programming, which offers load balancing and fault tolerance. MR-AVF is particularly simple to develop and it is shown to be highly scalable with respect to the number of cluster nodes. © 2008 IEEE.

Publication Date

11-24-2008

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Number of Pages

3298-3304

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2008.4634266

Socpus ID

56349095269 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/56349095269

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