Strong valid inequalities for Boolean logical pattern generation

Kedong Yan, Hong Seo Ryoo

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

0–1 multilinear programming (MP) captures the essence of pattern generation in logical analysis of data (LAD). This paper utilizes graph theoretic analysis of data to discover useful neighborhood properties among data for data reduction and multi-term linearization of the common constraint of an MP pattern generation model in a small number of stronger valid inequalities. This means that, with a systematic way to more efficiently generating Boolean logical patterns, LAD can be used for more effective analysis of data in practice. Mathematical properties and the utility of the new valid inequalities are illustrated on small examples and demonstrated through extensive experiments on 12 real-life data mining datasets.

Original languageEnglish
Pages (from-to)1-48
Number of pages48
JournalJournal of Global Optimization
DOIs
Publication statusAccepted/In press - 2017 Mar 20

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Valid Inequalities
Linearization
Data mining
Data reduction
Experiments
Programming
Data Reduction
Data Mining
Logic
Valid inequalities
Term
Graph in graph theory
Experiment

Keywords

  • 0–1 linearization
  • 0–1 multilinear programming
  • Boolean logic
  • Clique
  • Hypercube
  • Logical analysis of data
  • Pattern

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

Cite this

Strong valid inequalities for Boolean logical pattern generation. / Yan, Kedong; Ryoo, Hong Seo.

In: Journal of Global Optimization, 20.03.2017, p. 1-48.

Research output: Contribution to journalArticle

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