Attribute value pairs based on discernibility matrix for outliers detection

Misinem, Azuraliza Abu Bakar, Abdul Razak Hamdan, Faizah Shaari

Research output: Contribution to journalArticle

Abstract

Outlier mining is one important task in data mining and it has always been receiving attention from many researchers. The detection of outliers is found useful in many real applications like fraud detection and network intrusion. There are many outlier detection methods found in literature which include the frequent pattern generation and Rough Set based outlier detection. Although many methods have been proposed in data mining, the problems in detecting outliers efficiently continue especially in many real applications, due to the high dimensionality of huge data sets and high computational in processing. In this study, we proposed a method to detect outliers by discovering interesting attribute value pairs based on the Discernibility Attribute Value Matrix (DAV) in Rough Set Theory (RS). Interesting attribute value pairs (avp) are generated from the DAV Matrix. Two measures which are the support and interest value are used to measure the interestingness of the attributes. In order to detect outliers, a new measurement called the DAV Outlier factor (DAVOF) is proposed. In addition, an Average Ratio (AR), which measures the performance of the outlier detection method is also proposed. The DAV algorithm (DAVAlg) is compared with the FindFPOF and RSetAlg methods. The result shows that the DAVAlg outperforms the other two methods.

Original languageEnglish
Pages (from-to)623-633
Number of pages11
JournalJournal of Theoretical and Applied Information Technology
Volume66
Issue number2
Publication statusPublished - 20 Aug 2014

Fingerprint

Discernibility Matrix
Outlier Detection
Outlier
Attribute
Data mining
Data Mining
Rough set theory
Fraud Detection
Frequent Pattern
Rough Set Theory
Rough Set
Dimensionality
Mining
Continue
Processing

Keywords

  • Attributes value pairs (avp)
  • Discernibility attribute value (DAV) matrix
  • Outliers detection

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Attribute value pairs based on discernibility matrix for outliers detection. / Misinem, ; Abu Bakar, Azuraliza; Hamdan, Abdul Razak; Shaari, Faizah.

In: Journal of Theoretical and Applied Information Technology, Vol. 66, No. 2, 20.08.2014, p. 623-633.

Research output: Contribution to journalArticle

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