Cause-effect relationships and partially defined Boolean functions
Annals of Operations Research
Computational learning theory: an introduction
Computational learning theory: an introduction
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Complexity of identification and dualization of positive Boolean functions
Information and Computation
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
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
Error-free and best-fit extensions of partially defined Boolean functions
Information and Computation
Machine Learning
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
On Generating All Minimal Integer Solutions for a Monotone System of Linear Inequalities
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
Rough Sets and Ordinal Classification
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Monotonicity in Bayesian networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On data classification by iterative linear partitioning
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Computational aspects of monotone dualization: A brief survey
Discrete Applied Mathematics
Counting and enumerating aggregate classifiers
Discrete Applied Mathematics
On data classification by iterative linear partitioning
Discrete Applied Mathematics
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Many data‐analysis algorithms in machine learning, datamining and a variety of other disciplines essentially operate on discrete multi‐attribute data sets. By means of discretisation or binarization also numerical data sets can be successfully analysed. Therefore, in this paper we view/introduce the theory of (partially defined) discrete functions as an important theoretical tool for the analysis of multi‐attribute data sets. In particular we study monotone (partially defined) discrete functions. Compared with the theory of Boolean functions relatively little is known about (partially defined) monotone discrete functions. It appears that decision lists are useful for the representation of monotone discrete functions. Since dualization is an important tool in the theory of (monotone) Boolean functions, we study the interpretation and properties of the dual of a (monotone) binary or discrete function. We also introduce the dual of a pseudo‐Boolean function. The results are used to investigate extensions of partially defined monotone discrete functions and the identification of monotone discrete functions. In particular, we present a polynomial time algorithm for the identification of so‐called stable discrete functions.