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Comprehensive concept description based on association rules: A meta-learning approach.

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Title: Comprehensive concept description based on association rules: A meta-learning approach.
Authors: Berka, Petr1 berka@vse.cz
Source: Intelligent Data Analysis. 2018, Vol. 22 Issue 2, p325-344. 20p.
Subjects: Association rule mining, LISP (Computer program language), Machine learning, Data analysis, Medical databases
Abstract: This paper presents a novel approach to post-processing of association rules based on the idea of meta-learning. A subsequent association rule mining step is applied to the results of "standard" association rule mining. We thus obtain "rules about rules", which can help us better understand the association rules generated in the first step. We define various types of such meta-rules and report some experiments on benchmark data from the UCI Machine Learning Repository as well as on data from atherosclerosis risk domain. When evaluating the proposed method, we use the LISp-Miner system. [ABSTRACT FROM AUTHOR]
Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Comprehensive concept description based on association rules: A meta-learning approach.
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  Data: <searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22LISP+%28Computer+program+language%29%22">LISP (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+databases%22">Medical databases</searchLink>
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  Data: This paper presents a novel approach to post-processing of association rules based on the idea of meta-learning. A subsequent association rule mining step is applied to the results of "standard" association rule mining. We thus obtain "rules about rules", which can help us better understand the association rules generated in the first step. We define various types of such meta-rules and report some experiments on benchmark data from the UCI Machine Learning Repository as well as on data from atherosclerosis risk domain. When evaluating the proposed method, we use the LISp-Miner system. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3233/IDA-163307
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      – Code: eng
        Text: English
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        PageCount: 20
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      – SubjectFull: Association rule mining
        Type: general
      – SubjectFull: LISP (Computer program language)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Data analysis
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      – SubjectFull: Medical databases
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      – TitleFull: Comprehensive concept description based on association rules: A meta-learning approach.
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              Text: 2018
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