Mining Approximate Frequent Itemsets over Data Streams Using Window Sliding Techniques

Young-Hee Kim; Eunkyoung Park; Young‐Gab Kim

doi:10.1007/978-3-642-10583-8_7

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Mining Approximate Frequent Itemsets over Data Streams Using Window Sliding Techniques

Young-Hee Kim Eunkyoung Park Young‐Gab Kim

Year: 2009 Communications in computer and information science Pages: 49-56 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-10583-8_7

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Keywords:

Sliding window protocol Data stream mining Data mining Computer science Data stream Chernoff bound Set (abstract data type) Computation Window (computing) Database transaction Data set Algorithm Range (aeronautics) Database Artificial intelligence Engineering

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Data Mining Algorithms and Applications

Physical Sciences → Computer Science → Information Systems

Rough Sets and Fuzzy Logic

Physical Sciences → Computer Science → Computational Theory and Mathematics

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Mining Approximate Frequent Itemsets over Data Streams Using Window Sliding Techniques

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