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

Fingerprint

Association rules
Software engineering
Industry

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

Cite this

Saito, H., Monden, A., & Yucel, Z. (2018). Extended association rule mining with correlation functions. In Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018 (pp. 79-84). [8530696] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCD2018.2018.00020

Extended association rule mining with correlation functions. / Saito, Hidekazu; Monden, Akito; Yucel, Zeynep.

Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 79-84 8530696.

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

Saito, H, Monden, A & Yucel, Z 2018, Extended association rule mining with correlation functions. in Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018., 8530696, Institute of Electrical and Electronics Engineers Inc., pp. 79-84, 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018, Yonago, Japan, 7/10/18. https://doi.org/10.1109/BCD2018.2018.00020
Saito H, Monden A, Yucel Z. Extended association rule mining with correlation functions. In Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 79-84. 8530696 https://doi.org/10.1109/BCD2018.2018.00020
Saito, Hidekazu ; Monden, Akito ; Yucel, Zeynep. / Extended association rule mining with correlation functions. Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 79-84
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