Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
ACM Computing Surveys (CSUR)
A clustering strategy based on a formalism of the reproductive process in natural systems
SIGIR '79 Proceedings of the 2nd annual international ACM SIGIR conference on Information storage and retrieval: information implications into the eighties
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Prediction of Oligopeptide Conformations via Deterministic Global Optimization
Journal of Global Optimization
Ab initio Tertiary Structure Prediction of Proteins
Journal of Global Optimization
Rearrangement Clustering: Pitfalls, Remedies, and Applications
The Journal of Machine Learning Research
Journal of Global Optimization
A network flow model for biclustering via optimal re-ordering of data matrices
Journal of Global Optimization
Optimizing microwind rural electrification projects. A case study in Peru
Journal of Global Optimization
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In this article we present a computational study for solving the distance-dependent rearrangement clustering problem using mixed-integer linear programming (MILP). To address sparse data sets, we present an objective function for evaluating the pair-wise interactions between two elements as a function of the distance between them in the final ordering. The physical permutations of the rows and columns of the data matrix can be modeled using mixed-integer linear programming and we present three models based on (1) the relative ordering of elements, (2) the assignment of elements to a final position, and (3) the assignment of a distance between a pair of elements. These models can be augmented with the use of cutting planes and heuristic methods to increase computational efficiency. The performance of the models is compared for three distinct re-ordering problems corresponding to glass transition temperature data for polymers and two drug inhibition data matrices. The results of the comparative study suggest that the assignment model is the most effective for identifying the optimal re-ordering of rows and columns of sparse data matrices.