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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
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Volume 26, Issue 1


Reducing the Feature Space Using Constraint-Governed Association Rule Mining

  • Department of Computer Science, Mangalore University, Mangalagangothri, Mangalore, 574199, India
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ M. Umme Salma
  • Corresponding author
  • Department of Computer Science, Mangalore University, Mangalagangothri, Mangalore, 574199, India
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-02-04 | DOI: https://doi.org/10.1515/jisys-2015-0059


Recent advancements in science and technology and advances in the medical field have paved the way for the accumulation of huge amount of medical data in the digital repositories, where they are stored for future endeavors. Mining medical data is the most challenging task as the data are subjected to many social concerns and ethical issues. Moreover, medical data are more illegible as they contain many missing and misleading values and may sometimes be faulty. Thus, pre-processing tasks in medical data mining are of great importance, and the main focus is on feature selection, because the quality of the input determines the quality of the resultant data mining process. This paper provides insight to develop a feature selection process, where a data set subjected to constraint-governed association rule mining and interestingness measures results in a small feature subset capable of producing better classification results. From the results of the experimental study, the feature subset was reduced to more than 50% by applying syntax-governed constraints and dimensionality-governed constraints, and this resulted in a high-quality result. This approach yielded about 98% of classification accuracy for the Breast Cancer Surveillance Consortium (BCSC) data set.

Keywords: Data mining; association rules; constraint-based ARM; interestingness measures; feature selection; SVM

MSC 2010: 68U35


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About the article

Corresponding author: M. Umme Salma, Department of Computer Science, Mangalore University, Mangalagangothri, Mangalore, 574199, India, e-mail:

Received: 2014-12-31

Published Online: 2016-02-04

Published in Print: 2017-01-01

Funding: Maulana Azad National Fellowship for Minority Students, (Grant/Award Number: ‘F1-17/2013-14/MANF-2013-14-MUS-KAR-24350’).

Citation Information: Journal of Intelligent Systems, Volume 26, Issue 1, Pages 139–152, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2015-0059.

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