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Error-free and best-fit extensions of partially defined Boolean functions
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Pareto-optimal patterns in logical analysis of data
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Saturated systems of homogeneous boxes and the logical analysis of numerical data
Discrete Applied Mathematics
Subset-conjunctive rules for breast cancer diagnosis
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
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
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ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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The paper describes a new, logic-based methodology for analyzing observations. The key features of the Logical Analysis of Data (LAD) are the discovery of minimal sets of features necessary for explaining all observations and the detection of hidden patterns in the data capable of distinguishing observations describing 驴positive驴 outcome events from 驴negative驴 outcome events. Combinations of such patterns are used for developing general classification procedures. An implementation of this methodology is described in the paper along with the results of numerical experiments demonstrating the classification performance of LAD in comparison with the reported results of other procedures. In the final section, we describe three pilot studies on applications of LAD to oil exploration, psychometric testing, and the analysis of developments in the Chinese transitional economy. These pilot studies demonstrate not only the classification power of LAD, but also its flexibility and capability to provide solutions to various case-dependent problems.