A survey of interestingness measures for knowledge discovery

@article{McGarry2005ASO,
  title={A survey of interestingness measures for knowledge discovery},
  author={Kenneth J. McGarry},
  journal={The Knowledge Engineering Review},
  year={2005},
  volume={20},
  pages={39 - 61}
}
  • K. McGarry
  • Published 1 March 2005
  • Computer Science
  • The Knowledge Engineering Review
It is a well-known fact that the data mining process can generate many hundreds and often thousands of patterns from data. [] Key Method These so-called interestingness measures are generally divided into two categories: objective measures based on the statistical strengths or properties of the discovered patterns and subjective measures that are derived from the user's beliefs or expectations of their particular problem domain. We evaluate the strengths and weaknesses of the various interestingness measures…
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339 Citations
Highly Influential Citations
12
Background Citations
158
Methods Citations
37
Results Citations
3

339 Citations

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Citation Type

A Survey of Interestingness Measures for Association Rules
TLDR
This paper presents a review of the available literature on the various interestingness measures, and sums up twelve measure criteria which are concerned by many researchers and evaluates the strengths and weaknesses of the two categories of measures.
Probabilistic Measures for Interestingness of Deviations - A Survey
TLDR
A brief survey of the current state of literature around interestingness of deviations, i.e. outliers with specific interest around probabilistic measures using Bayesian belief networks is reviewed.
Knowledge discovery interestingness measures based on unexpectedness
TLDR
Different methods for assessing the unexpectedness of patterns with a special focus on frequent itemsets, tiles, association rules, and classification rules are surveyed, namely, syntactical and probabilistic approaches.
Modeling of the counter-examples and association rules interestingness measures behavior
TLDR
Three modelings of counter-examples are presented and the consequences of such modelizations on two important desired properties of association rules interestingness measures, that are the decrease with the number of counterexamples and the tolerance to the apparition of the first countereXamples are examined.
Subjective Interestingness in Association Rule Mining: A Theoretical Analysis
TLDR
This work proposes and construct a theoretical framework for studying subjective interestingness in association rule mining, and attempts to fit prior work done on subjectiveinterestingness into this framework, thus identifying relevant research gaps.
Ascertaining Data Mining Rules Using Statistical Approaches
TLDR
This research investigates how to combine data mining and statistical measurement techniques to arrive at more reliable and interesting set of rules, greatly essential to conquer the data overload in practical problems.
Are we really discovering ''interesting'' knowledge from data?
TLDR
A critical review of the literature on discovering comprehensible, interesting knowledge from data, focusing only on the problem of maximizing the accuracy of the discovered patterns, ignoring other important pattern-quality criteria that are user-oriented, such as comprehensibility and interestingness.
Behavior-based clustering and analysis of interestingness measures for association rule mining
TLDR
By clustering based on ranking behavior, this paper highlights, and formally prove, previously unreported equivalences among interestingness measures, and shows that domain knowledge is essential to the selection of an appropriate interestingness measure for a particular task and business objective.
Association Rule Interestingness Measures: Experimental and Theoretical Studies
TLDR
A formal and an experimental study of 20 measures of association rule interestingness measures and the properties are used in a multi-criteria decision analysis in order to select amongst the available measures the one or those that best take into account the user’s needs.
Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures
TLDR
With Interestingnesslab, a tool of the objective interestingness measures is developed in the R language that can automatically calculate the interestingness values of the patterns by using those measures, and is the framework for rapidly developing the applications related to objectiveinterestingness measures.
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References

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TLDR
The focus of the paper is on studying subjective measures of interestingness, which are classified into actionable and unexpected, and the relationship between them is examined.
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TLDR
This paper provides an overview of some existing measures of interestingness and comments on their properties and only focusses on objective measures ofinterestingness.
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TLDR
This work theoretically and empirically evaluate twelve diversity measures used as heuristic measures of interestingness for ranking summaries generated from databases, and describes five principles that any measure must satisfy to be considered useful forranking summaries.
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TLDR
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TLDR
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