Using Bloom Filters for Mining Top-k Frequent Itemsets in Data Streams

Young-Hee Kim; Kyung Soo Cho; Jae Yeol Yoon; Iee Joon Kim; Young‐Gab Kim

doi:10.1007/978-3-642-22339-6_25

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Using Bloom Filters for Mining Top-k Frequent Itemsets in Data Streams

Young-Hee Kim Kyung Soo Cho Jae Yeol Yoon Iee Joon Kim Young‐Gab Kim

Year: 2011 Communications in computer and information science Pages: 209-216 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-22339-6_25

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Keywords:

Bloom filter Computer science Data stream mining Data stream Computation Filter (signal processing) Data structure Data mining Workspace Parallel computing Theoretical computer science Algorithm Artificial intelligence Programming language

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Data Mining Algorithms and Applications

Physical Sciences → Computer Science → Information Systems

Data Management and Algorithms

Physical Sciences → Computer Science → Signal Processing

Rough Sets and Fuzzy Logic

Physical Sciences → Computer Science → Computational Theory and Mathematics

Using Bloom Filters for Mining Top-k Frequent Itemsets in Data Streams

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Citation History

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