On generating all maximal independent sets
Information Processing Letters
Cause-effect relationships and partially defined Boolean functions
Annals of Operations Research
Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Disjunctive and conjunctive normal forms of pseudo-Boolean functions
Discrete Applied Mathematics - Special issue on Boolean functions and related problems
The Maximum Box Problem and its Application to Data Analysis
Computational Optimization and Applications
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Pareto-optimal patterns in logical analysis of data
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Consensus algorithms for the generation of all maximal bicliques
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
Comprehensive vs. comprehensible classifiers in logical analysis of data
Discrete Applied Mathematics
Accelerated algorithm for pattern detection in logical analysis of data
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
A Robust Meta-classification Strategy for Cancer Diagnosis from Gene Expression Data
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Comparisons of classification methods in the original and pattern spaces
Expert Systems with Applications: An International Journal
Generalization error bounds for the logical analysis of data
Discrete Applied Mathematics
Compact MILP models for optimal and Pareto-optimal LAD patterns
Discrete Applied Mathematics
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In a finite dataset consisting of positive and negative observations represented as real valued n-vectors, a positive (negative) pattern is an interval in R^n with the property that it contains sufficiently many positive (negative) observations, and sufficiently few negative (positive) ones. A pattern is spanned if it does not include properly any other interval containing the same set of observations. Although large collections of spanned patterns can provide highly accurate classification models within the framework of the Logical Analysis of Data, no efficient method for their generation is currently known. We propose in this paper, an incrementally polynomial time algorithm for the generation of all spanned patterns in a dataset, which runs in linear time in the output; the algorithm resembles closely the Blake and Quine consensus method for finding the prime implicants of Boolean functions. The efficiency of the proposed algorithm is tested on various publicly available datasets. In the last part of the paper, we present the results of a series of computational experiments which show the high degree of robustness of spanned patterns.