TY - GEN
T1 - Extended association rule mining with correlation functions
AU - Saito, Hidekazu
AU - Monden, Akito
AU - Yucel, Zeynep
N1 - Funding Information:
ACKNOWLEDGMENTS This research was supported by JSPS KAKENHI Grant number 17K00102.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/9
Y1 - 2018/11/9
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.
KW - Association-rule-mining
KW - Data-mining
KW - Software-effort-estimation
UR - http://www.scopus.com/inward/record.url?scp=85058443606&partnerID=8YFLogxK
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U2 - 10.1109/BCD2018.2018.00020
DO - 10.1109/BCD2018.2018.00020
M3 - Conference contribution
AN - SCOPUS:85058443606
T3 - Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
SP - 79
EP - 84
BT - Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018
Y2 - 10 July 2018 through 12 July 2018
ER -