Large-scale clustering using mathematical programming

Gnägi, Mario; Baumann, Philipp (10 December 2017). Large-scale clustering using mathematical programming. In: Proceeding of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management. Singapore. 10.-13. Dezember. 10.1109/IEEM.2017.8289999

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Cluster analysis is a fundamental task in exploratory data analysis with a wide range of applications. Several clustering approaches based on mathematical programming have been proposed in the literature and were successfully used for small- and medium-scale data sets. However, mathematical programming-based clustering models are rarely used for large-scale data sets due to their extensive running time. In this paper, we propose a general scaling approach for existing mathematical programming-based clustering models that is based on the idea of replacing identical or nearly-identical objects by a small set of representatives. Our computational results indicate that the proposed scaling approach substantially reduces running time with a minor loss in clustering accuracy.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Financial Management > Professorship for Quantitative Methods in Business Administration

UniBE Contributor:

Gnägi, Mario and Baumann, Philipp

Subjects:

600 Technology > 650 Management & public relations

Language:

English

Submitter:

Larissa Notz

Date Deposited:

04 Apr 2018 08:14

Last Modified:

04 Nov 2019 18:06

Publisher DOI:

10.1109/IEEM.2017.8289999

BORIS DOI:

10.7892/boris.110081

URI:

https://boris.unibe.ch/id/eprint/110081

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