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 language | English |
|---|---|
| Pages (from-to) | 183-230 |
| Number of pages | 48 |
| Journal | Journal of Global Optimization |
| Volume | 69 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2017 Sept 1 |
Bibliographical note
Publisher Copyright:© 2017, Springer Science+Business Media New York.
Keywords
- 0–1 linearization
- 0–1 multilinear programming
- Boolean logic
- Clique
- Hypercube
- Logical analysis of data
- Pattern
ASJC Scopus subject areas
- Business, Management and Accounting (miscellaneous)
- Computer Science Applications
- Management Science and Operations Research
- Control and Optimization
- Applied Mathematics