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Mining frequent itemsets over data stream by matrix

Cheng Liang

Open publisher page 2 citations

Abstract

Mining frequent itemsets is a basic task of the data stream mining. Recently many approximate algorithms can mine frequent itemsets over data stream. However, these algorithms still can not efficiently reduce space and time cost. To improve the efficiency of mining frequent itemsets over data stream, matrix is imported as the synopsis data structure and a new algorithm of mining frequent itemsets is presented. Finally, experiments prove the efficiency of this algorithm.

About this research paper

What this paper is about

Mining frequent itemsets is a basic task of the data stream mining. Recently many approximate algorithms can mine frequent itemsets over data stream. However, these algorithms still can not efficiently reduce space and time cost. To improve the efficiency of mining frequent itemsets over data stream, matrix is imported as the synopsis data structure and a new algorithm of mining frequent itemsets is presented. Finally, experiments prove the efficiency of this algorithm.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Main findings

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Available abstract

Mining frequent itemsets is a basic task of the data stream mining. Recently many approximate algorithms can mine frequent itemsets over data stream. However, these algorithms still can not efficiently reduce space and time cost. To improve the efficiency of mining frequent itemsets over data stream, matrix is imported as the synopsis data structure and a new algorithm of mining frequent itemsets is presented. Finally, experiments prove the efficiency of this algorithm.

Key concepts: Computer science, Data mining, Data stream, Data stream mining, Task (project management), Matrix (chemical analysis), Efficient algorithm, Space (punctuation)

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