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2013 (Vol. 6, Issue: 08)
Article Information:

Outlier Detection Scoring Measurements Based on Frequent Pattern Technique

Aiman Moyaid Said, Dhanapal Durai Dominic and Brahim Belhaouari Samir
Corresponding Author:  Aiman Moyaid Said 

Key words:  Anomaly, frequent pattern mining, outlier detection, outlier measurement, , ,
Vol. 6 , (08): 1341-1347
Submitted Accepted Published
August 02, 2012 September 03, 2012 July 10, 2013

Outlier detection is one of the main data mining tasks. The outliers in data are more significant and interesting than common ones in a wide variety of application domains, such as fraud detection, intrusion detection, ecosystem disturbances and many others. Recently, a new trend for detecting the outlier by discovering frequent patterns (or frequent item sets) from the data set has been studied. In this study, we present a summarization and comparative study of the available outlier detection scoring measurements which are based on the frequent patterns discovery. The comparisons of the outlier detection scoring measurements are based on the detection effectiveness. The results of the comparison prove that this approach of outlier detection is a promising approach to be utilized in different domain applications.
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  Cite this Reference:
Aiman Moyaid Said, Dhanapal Durai Dominic and Brahim Belhaouari Samir, 2013. Outlier Detection Scoring Measurements Based on Frequent Pattern Technique.  Research Journal of Applied Sciences, Engineering and Technology, 6(08): 1341-1347.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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