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} }
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…
339 Citations
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References
SHOWING 1-10 OF 151 REFERENCES
What Makes Patterns Interesting in Knowledge Discovery Systems
- Computer ScienceIEEE Trans. Knowl. Data Eng.
- 1996
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.
Defining interestingness for association rules
- Computer Science
- 2003
This paper provides an overview of some existing measures of interestingness and comments on their properties and only focusses on objective measures ofinterestingness.
Measuring the interestingness of discovered knowledge: A principled approach
- Computer ScienceIntell. Data Anal.
- 2003
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.
Selecting the right interestingness measure for association patterns
- Computer ScienceKDD
- 2002
An overview of various measures proposed in the statistics, machine learning and data mining literature is presented and it is shown that each measure has different properties which make them useful for some application domains, but not for others.
Unexpectedness as a Measure of Interestingness in Knowledge Discovery
- Computer Science, BusinessDecis. Support Syst.
- 1999
On Subjective Measures of Interestingness in Knowledge Discovery
- Computer ScienceKDD
- 1995
The purpose of this paper is to lay the groundwork for a comprehensive study of subjective measures of interestingness and classify these measures into actionable and unexpected, and examine the relationship between them.
Finding Interesting Patterns Using User Expectations
- Computer ScienceIEEE Trans. Knowl. Data Eng.
- 1999
In this paper, a technique to prevent the user from being overwhelmed by the large number of patterns, techniques are needed to rank them according to their interestingness, called the user-expectation method.
Exception Rule Mining with a Relative Interestingness Measure
- Computer SciencePAKDD
- 2000
This paper presents a method for mining exception rules based on a novel measure which estimates interestingness relative to its corresponding common sense rule and reference rule and shows through the experiments how this proposed relative measure can give an unbiased estimate of relative interestingness in a rule considering already mined rules.
Interestingness via what is not interesting
- Computer ScienceKDD '99
- 1999
This work presents a simple and short process of eliminating a substantial portion of uninteresting association rules in a list outputted by a data-mining algorithm, and presents representative results of executions of the algorithm over three real databases.
A Metric for Selection of the Most Promising Rules
- Computer SciencePKDD
- 1998
A metric for selection of the n rules with the highest average distance between them is proposed and it is defended that applying the metric to select the rules that are more distant improves the system prediction capabilities against other criteria for rule selection.