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Market basket analysis with networks

Authors
  • Raeder, Troy1
  • Chawla, Nitesh V.1
  • 1 University of Notre Dame, Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, Notre Dame, IN, 46556, USA , Notre Dame (United States)
Type
Published Article
Journal
Social Network Analysis and Mining
Publisher
Springer Vienna
Publication Date
Aug 28, 2010
Volume
1
Issue
2
Pages
97–113
Identifiers
DOI: 10.1007/s13278-010-0003-7
Source
Springer Nature
Keywords
License
Yellow

Abstract

The field of market basket analysis, the search for meaningful associations in customer purchase data, is one of the oldest areas of data mining. The typical solution involves the mining and analysis of association rules, which take the form of statements such as “people who buy diapers are likely to buy beer”. It is well-known, however, that typical transaction datasets can support hundreds or thousands of obvious association rules for each interesting rule, and filtering through the rules is a non-trivial task (Klemettinen et al. In: Proceedings of CIKM, pp 401–407, 1994). One may use an interestingness measure to quantify the usefulness of various rules, but there is no single agreed-upon measure and different measures can result in very different rankings of association rules. In this work, we take a different approach to mining transaction data. By modeling the data as a product network, we discover expressive communities (clusters) in the data, which can then be targeted for further analysis. We demonstrate that our network based approach can concisely isolate influence among products, mitigating the need to search through massive lists of association rules. We develop an interestingness measure for communities of products and show that it isolates useful, actionable communities. Finally, we build upon our experience with product networks to propose a comprehensive analysis strategy by combining both traditional and network-based techniques. This framework is capable of generating insights that are difficult to achieve with traditional analysis methods.

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