A very large scale neighborhood search algorithm for the q-mode problem

被引：0
|
作者：
Kulkarni, Girish
Fathi, Yahya
机构：
[1] Fed Express, Memphis, TN USA
[2] N Carolina State Univ, Dept Ind Engn, Raleigh, NC 27695 USA
来源：
IIE TRANSACTIONS | 2007年 / 39卷 / 10期
基金：
美国国家科学基金会;
关键词：
cluster analysis; data mining; local search/local improvement;
D O I：
10.1080/07408170701416657
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The q-mode problem is a combinatorial optimization problem that arises in the context of partitioning a given collection of data vectors with categorical attributes. A neighborhood search algorithm is proposed for solving the q-mode problem. This algorithm is based on a very large scale neighborhood that is implicitly searched using network flow techniques. The algorithm is evaluated through a computational experiment using randomly generated instances. The results show that in general this algorithm obtains very-good-quality local optima, and that in instances with strong natural clusters the algorithm consistently finds optimal or near-optimal solutions.
引用
收藏
页码：971 / 984
页数：14
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