Extended association rule mining with correlation functions

Hidekazu Saito, Akito Monden, Zeynep Yucel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A →Correl(X, Y ) where Correl(X, Y ) is a correlation function with two variables X and Y. By this extension, data analysts can discover the condition A that lead to low (or high) correlation between two given variables from a large dataset. In order to show the efficacy of the proposed method, a case study is performed on an industry dataset of software developments, assuming the scenario of discovering a condition, where software development effort is predictable (or unpredictable) from the size of the project, i.e. there exists a significantly high (or low) correlation between size and effort. Since such a condition cannot be obtained by conventional association rule mining, we confirm the efficiency of the proposed extended association rule mining.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages79-84
Number of pages6
ISBN (Electronic)9781538656051
DOIs
Publication statusPublished - Nov 9 2018
Event3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018 - Yonago, Japan
Duration: Jul 10 2018Jul 12 2018

Other

Other3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
CountryJapan
CityYonago
Period7/10/187/12/18

Keywords

  • Association-rule-mining
  • Data-mining
  • Software-effort-estimation

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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