Communications of the ACM
Cause-effect relationships and partially defined Boolean functions
Annals of Operations Research
Structure identification in relational data
Artificial Intelligence - Special volume on constraint-based reasoning
A Gray code for the ideals of a forest poset
Journal of Algorithms
Decomposability of partially defined Boolean functions
Discrete Applied Mathematics - Special volume on partitioning and decomposition in combinatorial optimization
Complexity of identification and dualization of positive Boolean functions
Information and Computation
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
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Error-free and best-fit extensions of partially defined Boolean functions
Information and Computation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
On connected Boolean functions
Discrete Applied Mathematics - Special issue on the satisfiability problem and Boolean functions
Convexity and logical analysis of data
Theoretical Computer Science
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
Machine Learning
Machine Learning
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)
Spanned patterns for the logical analysis of data
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Accelerated algorithm for pattern detection in logical analysis of data
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Discrete Applied Mathematics
Comprehensive vs. comprehensible classifiers in logical analysis of data
Discrete Applied Mathematics
Logical analysis of diffuse large B-cell lymphomas
Artificial Intelligence in Medicine
A logical analysis of banks' financial strength ratings
Expert Systems with Applications: An International Journal
Compact MILP models for optimal and Pareto-optimal LAD patterns
Discrete Applied Mathematics
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Logical analysis of data (LAD) is a special data analysis methodology which combines ideas and concepts from optimization, combinatorics, and Boolean functions. The central concept in LAD is that of patterns, or rules, which were found to play a critical role in classification, ranked regression, clustering, detection of subclasses, feature selection and other problems. The research area of LAD was defined and initiated by Peter L. Hammer, who was the catalyst of the LAD oriented research for decades, and whose consistent vision and efforts helped the methodology to move from theory to data analysis applications, to achieve maturity and to be successful in many medical, industrial and economics case studies. This overview presents some of the basic aspects of LAD, from the definition of the main concepts to the efficient algorithms for pattern generation, and from the complexity analysis of the difficult problems embedded in LAD to its biomedical applications. We focus in this paper only on some recent developments in LAD which were of particular interest to Peter L. Hammer, who played a key role in obtaining all the results described here. The presentation in this overview is based on the original publications of Peter L. Hammer and his co-authors. We dedicate this paper to the memory of Peter L. Hammer.