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Subjective Interestingness in Association Rule Mining: A Theoretical Analysis

  • Rupal Sethi
  • B. ShekarEmail author
Chapter
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 21)

Abstract

The main aim of “Knowledge Discovery in Databases” is to extract and interpret interesting patterns present in real-world datasets. Measures to identify interesting patterns (called Interestingness Measures) may be categorized on the basis of statistical significance (Objective Measures), or on the basis of data and subjectivity of the user (Subjective Measures) which includes user’s domain knowledge. Three major steps in dealing with subjective measures are (1) knowledge acquisition from the user in terms of his beliefs, (2) the matching methodology for comparing generated association rules and user’s belief, and (3) generation of interesting rules that may be unexpected, novel or actionable. We propose and construct a theoretical framework for studying subjective interestingness in association rule mining, which takes care of these steps. We attempt to fit prior work done on subjective interestingness into this framework, thus identifying relevant research gaps. The notion of subjective interestingness confines to knowledge discovery by managers in a supermarket focusing on their expectations based on the data available. Perceptions behind customer purchases are not explicitly considered. We pose a major research question in subjective interestingness: What is the nature of subjective interestingness among associations of items, in terms of manager’s expectations and customers’ purchase patterns?

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Decision Sciences and Information Systems AreaIndian Institute of Management BangaloreBangaloreIndia

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