Papers

Peer-reviewed Last author
Jul, 2018

Extended Association Rule Mining with Correlation Functions

2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)
  • Hidekazu Saito
  • ,
  • Akito Monden
  • ,
  • Zeynep Yucel

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
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
  • DOI : 10.1109/bcd2018.2018.00020
  • ISBN : 9781538656051
  • DBLP ID : conf/bcs/SaitoMY18
  • SCOPUS ID : 85058443606
  • Web of Science ID : WOS:000494717600013

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