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A comparative study for outlier detection techniques in data mining

Bakar, Zuriana Abu and Mohemad, R and Ahmad, A and Mat Deris, Mustafa Mat (2006) A comparative study for outlier detection techniques in data mining. In: 2006 IEEE Conference on Cybernetics and Intelligent Systems, 7-9 June 2006.

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Official URL: http://dx.doi.org/10.1109/ICCIS.2006.252287

Abstract

Existing studies in data mining mostly focus on finding patterns in large datasets and further using it for organizational decision making. However, finding such exceptions and outliers has not yet received as much attention in the data mining field as some other topics have, such as association rules, classification and clustering. Thus, this paper describes the performance of control chart, linear regression, and Manhattan distance techniques for outlier detection in data mining. Experimental studies show that outlier detection technique using control chart is better than the technique modeled from linear regression because the number of outlier data detected by control chart is smaller than linear regression. Further, experimental studies shows that Manhattan distance technique outperformed compared with the other techniques when the threshold values increased.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Clustering, data mining, outlier
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:7808
Deposited By: Maznira Sylvia Azra Mansor
Deposited On:23 Jan 2009 00:52
Last Modified:02 Jun 2010 01:48

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