Identifying bundles of product options using mutual information clustering

Claudia Perlich, Saharon Rosset

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Mass-produced goods tend to be highly standardized in order to maximize manufacturing efficiencies. Some high-value goods with limited production quantities remain much less standardized and each sale can be configured to meet the specific requirements of the customer. In this work we suggest a novel methodology to reduce the number of options for complex product configurations by identifying meaningful sets of options that exhibit strong empirical dependencies in previous customer orders. Our approach explores different measures from statistics and information theory to capture the degree of interdependence between the choices for any pair of product components. We use hierarchical clustering to identify meaningful sets of components that can be combined to decrease the number of unique product specifications and increase production standardization. The focus of our analysis is on the influence of different similarity measure - including chisquared statistics and versions of mutual information on the ability of the clustering to find meaningful clusters.

Original languageEnglish
Title of host publicationProceedings of the 7th SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics (SIAM)
Pages390-397
Number of pages8
ISBN (Print)9780898716306
DOIs
StatePublished - 2007
Externally publishedYes
Event7th SIAM International Conference on Data Mining - Minneapolis, MN, United States
Duration: 26 Apr 200728 Apr 2007

Publication series

NameProceedings of the 7th SIAM International Conference on Data Mining

Conference

Conference7th SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityMinneapolis, MN
Period26/04/0728/04/07

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