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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/964

Title: Defining Interestigness for Association Rules
Authors: Brijs, Tom
Vanhoof, Koen
Wets, Geert
Keywords: Association Rules
Data Mining
Issue Date: 2003
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: Interestingness in Association Rules has been a major topic of research in the past decade. The reason is that the strength of association rules, i.e. its ability to discover ALL patterns given some thresholds on support and confidence, is also its weakness. Indeed, a typical association rules analysis on real data often results in hundreds or thousands of patterns creating a data mining problem of the second order. In other words, it is not straightforward to determine which of those rules are interesting for the end-user. This paper provides an overview of some existing measures of interestingness and we will comment on their properties. In general, interestingness measures can be divided into objective and subjective measures. Objective measures tend to express interestingness by means of statistical or mathematical criteria, whereas subjective measures of interestingness aim at capturing more practical criteria that should be taken into account, such as unexpectedness or actionability of rules. This paper only focusses on objective measures of interestingness.
URI: http://hdl.handle.net/10525/964
ISSN: 1313-0463
Appears in Collections:Volume 10 Number 4

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