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Research Article Open access CC BY 4.0

Approximate Functional Dependencies Mining Using Association Rules Specificity Interestingness Measure

Jalal Omer Atoum

Journal of Advances in Mathematics and Computer Science · pp. 1–10 · Published 5 Jul 2016

10.9734/BJMCS/2016/25206

Abstract

Mining Approximate Functional Dependencies (AFDs) from a database may produce valuable interesting relationships among its variables that would be beneficial in several domain applications such as marketing, financial data analysis, biological data analysis, and intrusion detection. Mining of association rules is concerned with extracting new knowledge from databases in the form of patterns and associations among data items. The mining of AFDs still posing special set of challenges such as the space and time requirements in addition to the quality of the discovered AFDs. In this paper, an approach for AFDs mining is being developed through employing the specificity interestingness measure and its monotonic property used in some association rules mining algorithms. This approach has been tested on a set of test bed of datasets. The results showed an improvement in time requirements and in the number of mined AFDs.

Approximate functional dependencies mining AFD interestingness measures specificity measure

References (12)

  1. 1 Fast Algorithms for Mining Association Rules in Large Databases
  2. 2 Interestingness measures for data mining [DOI]
  3. 3 Discovery, Analysis, and Presentation of Strong Rules
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Cited by 2

Mining Approximate Primary Functional Dependency on Web Tables

Siyu CHEN, Ning WANG, Mengmeng ZHANG · IEICE Transactions on Information and Systems · 2019

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