A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks

Integer programming models for clustering have applications in diverse fields addressing many problems such as market segmentation and location of facilities. Integer programming models are flexible in expressing objectives subject to some special constraints of the clustering problem. They are also...

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Veröffentlicht in:Journal of optimization theory and applications Jg. 176; H. 2; S. 492 - 508
Hauptverfasser: Pirim, Harun, Eksioglu, Burak, Glover, Fred W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.02.2018
Springer Nature B.V
Schlagworte:
Applications of Mathematics
Bioinformatics
Calculus of Variations and Optimal Control; Optimization
Clustering
Engineering
Integer programming
Linear programming
Mathematical programming
Mathematics
Mathematics and Statistics
Mixed integer
Operations Research/Decision Theory
Optimization
Production scheduling
Theory of Computation
05C12
90C35
68R05
90C11
Mixed integer programming
Social networks
90C90
90C05
90C27
Clustering
68R10
ISSN:0022-3239, 1573-2878
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Zusammenfassung:Integer programming models for clustering have applications in diverse fields addressing many problems such as market segmentation and location of facilities. Integer programming models are flexible in expressing objectives subject to some special constraints of the clustering problem. They are also important for guiding clustering algorithms that are capable of handling high-dimensional data. Here, we present a novel mixed integer linear programming model especially for clustering relational networks, which have important applications in social sciences and bioinformatics. Our model is applied to several social network data sets to demonstrate its ability to detect natural network structures.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-017-1213-1