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2010•Computer Engineering and Applications JournalRequires access

Research on mining frequent itemsets in data streams

Caixia Meng

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Abstract

According to the characteristic of data streams,the paper proposes FP-SegCount algorithm for mining frequent itemsets from data streams.The algorithm partitions the data stream and uses modified FP-growth algorithm to mining frequent itemsets in every segment.And then,it counts itemsets in Count Min Sketch.The algorithm solves the problem of compressed statistic and effective computation.Through experimentation and comparision with FP-DS algorithm,FP-SegCount algorithm has a good time efficiency.

About this research paper

What this paper is about

According to the characteristic of data streams,the paper proposes FP-SegCount algorithm for mining frequent itemsets from data streams.The algorithm partitions the data stream and uses modified FP-growth algorithm to mining frequent itemsets in every segment.And then,it counts itemsets in Count Min Sketch.The algorithm solves the problem of compressed statistic and effective computation.Through experimentation and comparision with FP-DS algorithm,FP-SegCount algorithm has a good time efficiency.

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Key contribution

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Method / approach

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Available abstract

According to the characteristic of data streams,the paper proposes FP-SegCount algorithm for mining frequent itemsets from data streams.The algorithm partitions the data stream and uses modified FP-growth algorithm to mining frequent itemsets in every segment.And then,it counts itemsets in Count Min Sketch.The algorithm solves the problem of compressed statistic and effective computation.Through experimentation and comparision with FP-DS algorithm,FP-SegCount algorithm has a good time efficiency.

Key concepts: Data stream mining, Computer science, Data mining, Statistic, Computation, Data stream, Sketch, STREAMS

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