Rate of change analysis for interestingness measures View Full Text


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Article Info

DATE

2019-03-20

AUTHORS

Nandan Sudarsanam, Nishanth Kumar, Abhishek Sharma, Balaraman Ravindran

ABSTRACT

The use of association rule mining techniques in diverse contexts and domains has resulted in the creation of numerous interestingness measures. This, in turn, has motivated researchers to come up with various classification schemes for these measures. One popular approach to classify the objective measures is to assess the set of mathematical properties they satisfy in order to help practitioners select the right measure for a given problem. In this research, we discuss the insufficiency of the existing properties in the literature to capture certain behaviors of interestingness measures. This motivates us to adopt an approach where a measure is described by how it varies if there is a unit change in the frequency count (f11,f10,f01,f00), at different preexisting states of the counts. This rate of change analysis is formally defined as the first partial derivative of the measure with respect to the various frequency counts. We use this analysis to define two novel properties, unit-null asymptotic invariance (UNAI) and unit-null zero rate (UNZR). UNAI looks at the asymptotic effect of adding frequency patterns, while UNZR looks at the initial effect of adding frequency patterns when they do not preexist in the dataset. We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties. We also present multiple empirical studies, involving both synthetic and real-world datasets, which are used to cluster various measures according to the rule ranking patterns of the measures. The study concludes with the observation that classification of measures using the empirical clusters shares significant similarities to the classification of measures done through the properties presented in this research. More... »

PAGES

1-20

References to SciGraph publications

  • 2007-08. Generalizing the notion of confidence in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2007. A Unified View of Objective Interestingness Measures in MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION
  • 2012-01. Mining top−k frequent patterns without minimum support threshold in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2014-07. Behavior-based clustering and analysis of interestingness measures for association rule mining in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2014-02. Boolean factors as a means of clustering of interestingness measures of association rules in ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
  • 2004. A Clustering of Interestingness Measures in DISCOVERY SCIENCE
  • 2011-01. Methods for mining frequent items in data streams: an overview in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2007. Choosing the Right Lens: Finding What is Interesting in Data Mining in QUALITY MEASURES IN DATA MINING
  • 2007. A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study in QUALITY MEASURES IN DATA MINING
  • 2007. Association Rule Interestingness Measures: Experimental and Theoretical Studies in QUALITY MEASURES IN DATA MINING
  • 2010. A Robustness Measure of Association Rules in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 1966-12. The GUHA method of automatic hypotheses determination in COMPUTING
  • 2008-05. Computing the minimum-support for mining frequent patterns in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2007. Association Mining in Large Databases: A Re-examination of Its Measures in KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2007
  • 2008-01. Top 10 algorithms in data mining in KNOWLEDGE AND INFORMATION SYSTEMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10115-019-01352-3

    DOI

    http://dx.doi.org/10.1007/s10115-019-01352-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112879545


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