Adaptive fuzzy partitions for evolving association rules in big data stream

DOI10.1016/J.IJAR.2017.11.014zbMath1452.68173OpenAlexW2773958476MaRDI QIDQ1726413

Jorge Casillas, Elena Ruiz

Publication date: 20 February 2019

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2017.11.014

zbMATH Keywords

real-time systemselectroencephalographyincremental learningassociation rulesgenetic fuzzy systemsdata stream mining

Mathematics Subject Classification ID

Computational aspects of data analysis and big data (68T09)

Related Items (1)

Dual incremental fuzzy schemes for frequent itemsets discovery in streaming numeric data

Uses Software

• MAX-FISM

Cites Work

• MAX-FISM: mining (recently) maximal frequent itemsets over data streams using the sliding window model
• Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
• Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining.
• Mining Associated Patterns from Wireless Sensor Networks
• Knowledge Discovery from Data Streams
• TRADE-OFF BETWEEN COMPUTATION TIME AND NUMBER OF RULES FOR FUZZY MINING FROM QUANTITATIVE DATA
• Mining association rules from quantitative data
• Learning in Non-Stationary Environments
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