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A New Improved Apriori Algorithm Based on Compression Matrix

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Advanced Data Mining and Applications (ADMA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8933))

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Abstract

The existing Apriori algorithm based on matrix still has the problems that the candidate itemsets are too large and matrix takes up too much memory space. To solve these problems, an improved Apriori algorithm based on compression matrix is proposed. The improvement ideas of this algorithm are as follows: (1) reducing the times of scanning matrix set during compressing by adding two arrays to record the counts of 1 in the row and column; (2) minimizing the scale of matrix and improving space utilization by deleting the itemsets which cannot be connected and the infrequent itemsets in compressing matrix; (3) decreasing the errors of the mining result by changing the condition of deleting the unnecessary transaction column;(4) reducing the cycling number of algorithm by changing the stopping condition of program. Instance analysis and experimental results show that the proposed algorithm can accurately and efficiently mines all frequent itemsets in transaction database, and improves the efficiency of mining association rules.

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Author information

Authors and Affiliations

  1. School of Computer, Electronics and Information, Guangxi University, 530004, Nanning, China

    Taoshen Li & Dan Luo

Authors
  1. Taoshen Li
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  2. Dan Luo
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Editor information

Editors and Affiliations

  1. Sun Yat-sen University, Guangzhou, P.R. China

    Xudong Luo

  2. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Jeffrey Xu Yu

  3. Guanxi Normal University, Guilin, P.R. China

    Zhi Li

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Li, T., Luo, D. (2014). A New Improved Apriori Algorithm Based on Compression Matrix. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-14717-8_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14716-1

  • Online ISBN: 978-3-319-14717-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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