An Exact Algorithm for the Two-Mode KL-Means Partitioning Problem

Abstract

Two-mode partitioning applications are increasingly common in the physical and social sciences with a variety of models and methods spanning these applications. Two-mode KL-means partitioning (TMKLMP) is one type of two-mode partitioning model with a conceptual appeal that stems largely from the fact that it is a generalization of the ubiquitous (one-mode) K-means clustering problem. A number of heuristic methods have been proposed for TMKLMP, ranging from a two-mode version of the K-means heuristic to metaheuristic approaches based on simulated annealing, genetic algorithms, variable neighborhood search, fuzzy steps, and tabu search. We present an exact algorithm for TMKLMP based on branch-and-bound programming and demonstrate its utility for the clustering of brand switching, manufacturing cell formation, and journal citation data. Although the proposed branchand-bound algorithm does not obviate the need for approximation methods for large two-mode data sets, it does provide a first step in the development of methods that afford a guarantee of globally-optimal solutions for TMKLMP.