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Subjective Interestingness in Association Rule Mining: A Theoretical Analysis

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Digital Business

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((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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Notes

  1. 1.

    Here we comment on the methodology adopted by GFT and not on the failure of the project.

  2. 2.

    https://www.merriam-webster.com/dictionary/interest, Accessed on 20th March 2017

  3. 3.

    The word “evidence” has its roots in epistemology and philosophy of science. Jaegwon Kim in his book ‘What is “Naturalized Epistemology”? defines evidence as justification of a belief.

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Authors and Affiliations

  1. Decision Sciences and Information Systems Area, Indian Institute of Management Bangalore, Bangalore, 560076, India

    Rupal Sethi & B. Shekar

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  1. Rupal Sethi
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  2. B. Shekar
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Correspondence to B. Shekar .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering and Technology, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

  3. Business Systems and Analytics Department, La Salle University, Philadelphia, Pennsylvania, USA

    Madjid Tavana

  4. University Politehnica of Bucharest, Bucharest, Romania

    Florin Popentiu-Vlădicescu

  5. Shenyang Jianzhu University, Shenyang, China

    Feng Qiao

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Sethi, R., Shekar, B. (2019). Subjective Interestingness in Association Rule Mining: A Theoretical Analysis. In: Patnaik, S., Yang, XS., Tavana, M., Popentiu-Vlădicescu, F., Qiao, F. (eds) Digital Business. Lecture Notes on Data Engineering and Communications Technologies, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-93940-7_15

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