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, Baumann, Philipp |
Subjects: |
600 Technology > 650 Management & public relations |
Language: |
English |
Submitter: |
Juliana Kathrin Moser-Zurbrügg |
Date Deposited: |
04 Apr 2018 08:14 |
Last Modified: |
05 Dec 2022 15:10 |
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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