Frequent positive and negative (FPN) itemset approach for outlier detection

Anis Suhailis Abdul Kadir, Azuraliza Abu Bakar, Abdul Razak Hamdan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The outlier detection has many applications and research attention to it is increasing. The detection rate is a significant measure for outlier mining that evaluates the outlier detection algorithms' performance. The problem is especially challenging because of the difficulty of defining a significant outlier measure in order to have a better detection rate. This paper proposes a novel approach for outlier detection with consideration of frequent negative itemset. This approach also produces positive itemset together with negative itemset. The knowledge and interesting pattern generated from frequent positive and negative (FPN) itemsets confidently enhances the outlier detection task in this method. The FPN itemset helps identification of transactions that are rare and in conflict with each other. However, discovering negative itemsets remains a challenge. To further investigate the potential knowledge of frequent negative itemsets in outlier detection, an experiment is conducted using the UCI datasets. The FPN itemset approach obtains better detection rate compared to other algorithms for majority datasets, indicating that the proposed approach is a promising approach in solving outlier detection problems.

Original languageEnglish
Pages (from-to)1049-1065
Number of pages17
JournalIntelligent Data Analysis
Volume18
Issue number6
DOIs
Publication statusPublished - 2014

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Outlier Detection
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Keywords

  • Frequent positive and negative itemset
  • Negative association rule
  • Negative itemset
  • Outlier detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition

Cite this

Frequent positive and negative (FPN) itemset approach for outlier detection. / Kadir, Anis Suhailis Abdul; Abu Bakar, Azuraliza; Hamdan, Abdul Razak.

In: Intelligent Data Analysis, Vol. 18, No. 6, 2014, p. 1049-1065.

Research output: Contribution to journalArticle

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