Extended association rule mining with correlation functions

Hidekazu Saito; Akito Monden; Zeynep Yucel

doi:10.1109/BCD2018.2018.00020

Extended association rule mining with correlation functions

Hidekazu Saito, Akito Monden, Zeynep Yucel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (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 language	English
Title of host publication	Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	79-84
Number of pages	6
ISBN (Electronic)	9781538656051
DOIs	https://doi.org/10.1109/BCD2018.2018.00020
Publication status	Published - Nov 9 2018
Event	3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018 - Yonago, Japan Duration: Jul 10 2018 → Jul 12 2018

Publication series

Name	Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018

Other

Other	3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
Country/Territory	Japan
City	Yonago
Period	7/10/18 → 7/12/18

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

Access to Document

10.1109/BCD2018.2018.00020

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). Article 8530696 (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.. 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 (Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference 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, 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., 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. (Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018). doi: 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 (Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018).

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title = "Extended association rule mining with correlation functions",

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.",

keywords = "Association-rule-mining, Data-mining, Software-effort-estimation",

author = "Hidekazu Saito and Akito Monden and Zeynep Yucel",

note = "Funding Information: ACKNOWLEDGMENTS This research was supported by JSPS KAKENHI Grant number 17K00102. Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018 ; Conference date: 10-07-2018 Through 12-07-2018",

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N2 - 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.

AB - 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.

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Extended association rule mining with correlation functions

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Keywords

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