A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks

• Published in: Journal of optimization theory and applications Vol. 176; no. 2; pp. 492 - 508
• Main Authors: Pirim, Harun, Eksioglu, Burak, Glover, Fred W.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2018; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s10957-017-1213-1

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.