Effective approximation of parametrized closure systems over transactional data streams

DOI10.1007/s10994-019-05827-wzbMath1497.68440OpenAlexW2971194171WikidataQ127322012 ScholiaQ127322012MaRDI QIDQ782465

Tamás L. Horváth, Stefan Wrobel, Daniel Trabold

Publication date: 27 July 2020

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-019-05827-w

zbMATH Keywords

data streamsitemset miningclosed itemsetsconcept drift detection

Mathematics Subject Classification ID

Database theory (68P15) Learning and adaptive systems in artificial intelligence (68T05) Online algorithms; streaming algorithms (68W27)

Uses Software

• UCI-ml
• StreamKrimp

Cites Work

• Unnamed Item
• Listing closed sets of strongly accessible set systems with applications to data mining
• Mining frequent closed itemsets from a landmark window over online data streams
• Finding all closed sets: A general approach
• On maximal frequent and minimal infrequent sets in binary matrices
• Probability inequalities for the sum in sampling without replacement
• Queries and concept learning
• Random sampling with a reservoir
• A survey on concept drift adaptation
• Probability Inequalities for Sums of Bounded Random Variables
• Formal Concept Analysis

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