Sampling frequent and minimal Boolean patterns: theory and application in classification

DOI10.1007/s10618-015-0409-yzbMath1411.68114OpenAlexW1994972455MaRDI QIDQ1741140

Geng Li, Mohammed Javeed Zaki

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-015-0409-y

zbMATH Keywords

classificationMarkov chain Monte Carlofrequent pattern miningminimal generatorsdisjunctive patternsminimal Boolean expressionspattern sampling

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery ⋮ Interpretation of text patterns

Uses Software

• UCI-ml
• LIBSVM
• BLOSOM
• TitanicOR
• Krimp

Cites Work

• Krimp: mining itemsets that compress
• Disclosed: an efficient depth-first, top-down algorithm for mining disjunctive closed itemsets in high-dimensional data
• Exact learning Boolean functions via the monotone theory
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Computing iceberg concept lattices with Titanic
• ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns
• Simulation and the Monte Carlo Method
• Discovery Science
• Generalized disjunction-free representation of frequent patterns with negation
• Knowledge Discovery in Inductive Databases
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