Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
MATLAB Primer
Visual Explorations in Finance
Visual Explorations in Finance
A visualization model based on adjacency data
Decision Support Systems
A Handbook of Statistical Analyses Using SPSS
A Handbook of Statistical Analyses Using SPSS
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
A comparison of problem decomposition techniques for the FAP
Journal of Heuristics
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Data visualization techniques have become important tools for analyzing large multidimensional data sets and providing insights with respect to scientific, economic, and engineering applications. Typically, these visualization applications are modeled and solved using nonlinear optimization techniques. In this paper, we propose a discretization of the data visualization problem that allows us to formulate it as a quadratic assignment problem. However, this formulation is computationally difficult to solve optimally using an exact approach. Consequently, we investigate the use of a local search technique for the data visualization problem. The space in which the data points are to be embedded can be discretized using an n × n lattice. Conducting a local search on this n × n lattice is computationally ineffective. Instead, we propose a divide-and-conquer local search approach that refines the lattice at each step. We show that this approach is much faster than conducting local search on the entire n × n lattice and, in general, it generates higher quality solutions. We envision two uses of our divide-and-conquer local search heuristic: (1) as a stand-alone approach for data visualization, and (2) to provide a good approximate starting solution for a nonlinear algorithm.