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The Maximum Box Problem and its Application to Data Analysis
Authors:Jonathan Eckstein  Peter L Hammer  Ying Liu  Mikhail Nediak  Bruno Simeone
Institution:(1) Rutgers Business School and RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854, USA;(2) RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854, USA;(3) Department of Statistics, ldquoLa Sapienzardquo, University, Piazzale Aldo Moro 5, 00185 Rome, Italy
Abstract:Given two finite sets of points X + and X in 
$$\mathbb{R}^n$$
n , the maximum box problem consists of finding an interval (ldquoboxrdquo) B = {x : l le x le u} such that B cap X = emptyv, and the cardinality of B cap X + is maximized. A simple generalization can be obtained by instead maximizing a weighted sum of the elements of B cap X +. While polynomial for any fixed n, the maximum box problem is 
$$ {\mathcal{N}}{\mathcal{P}}$$
-hard in general. We construct an efficient branch-and-bound algorithm for this problem and apply it to a standard problem in data analysis. We test this method on nine data sets, seven of which are drawn from the UCI standard machine learning repository.
Keywords:discrete optimization  branch and bound  data analysis  patterns
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