A New Outlier Detection Algorithms Based on Markov Chain

Jun Yang; Ying Long Wang

doi:10.4028/www.scientific.net/AMR.366.456

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 366A New Outlier Detection Algorithms Based on Markov...

A New Outlier Detection Algorithms Based on Markov Chain

Abstract:

Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. In high-dimensional data, these approaches are bound to deteriorate due to the notorious “curse of dimensionality”. In this paper, we propose a novel approach named ODMC (Outlier Detection Based On Markov Chain)，the effects of the “curse of dimensionality” are alleviated compared to purely distance-based approaches. A main advantage of our new approach is that our method is to use a major feature of an undirected weighted graph to calculate the outlier degree of each node, In a thorough experimental evaluation, we compare ODMC to the ABOD and FindFPOF for various artificial and real data set and show ODMC to perform especially well on high-dimensional data.

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Periodical:

Advanced Materials Research (Volume 366)

Pages:

456-459

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.366.456

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Online since:

October 2011

Authors:

Jun Yang, Ying Long Wang

Keywords:

Data Mining (DM), High Dimensional Data Set, Markov Chain (MC), Outlier

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