Jul, 2018
Extended Association Rule Mining with Correlation Functions
2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)
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- First page
- 79
- Last page
- 84
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1109/bcd2018.2018.00020
- Publisher
- IEEE
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.
- Link information
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- DOI
- https://doi.org/10.1109/bcd2018.2018.00020
- DBLP
- https://dblp.uni-trier.de/rec/conf/bcs/SaitoMY18
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000494717600013&DestApp=WOS_CPL
- URL
- http://xplorestaging.ieee.org/ielx7/8528918/8530355/08530696.pdf?arnumber=8530696
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85058443606&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85058443606&origin=inward
- ID information
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- DOI : 10.1109/bcd2018.2018.00020
- ISBN : 9781538656051
- DBLP ID : conf/bcs/SaitoMY18
- SCOPUS ID : 85058443606
- Web of Science ID : WOS:000494717600013