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Improved Algorithm for Mining N-Most Interesting Itemsets

  • Conference paper
Emerging Research in Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 237))

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Abstract

BOMO algorithm constructs conditional FP-Tree recursively so that it requires more memory and CPU resources. To solve this problem, an algorithm for mining N-most interesting itemsets based on COFI-Tree is presented. This algorithm adopts COFI-Tree. COFI-Tree doesn’t need to construct conditional FP-Tree recursively and there is only one COFI-Tree in memory at a time. Experiment shows that (1) the new algorithm based on COFI-tree performs faster than current best algorithm BOMO;(2) the algorithm has good performance for large data set, especially it shows the best when for k value is smaller than 4.

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Author information

Authors and Affiliations

  1. College of Textile Engineering and Art, Taiyuan University of Technology, 030600, JinZhong, China

    Xiaohong Cui, Jihai Xiao & Lijun Sang

  2. College of Computer Science and Technology, Taiyuan University of Technology, 030024, TaiYuan, China

    Junjie Chen

Authors
  1. Xiaohong Cui
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  2. Jihai Xiao
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  3. Junjie Chen
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  4. Lijun Sang
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Editor information

Editors and Affiliations

  1. School of Business Information technology, RMIT University, GPO Box 2476V, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  2. Department of Computer Science and Technology, Tongji University, Shanghai, China

    Duoqian Miao

  3. Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan, Hong Kong, SAR, China

    Fu Lee Wang

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

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© 2011 Springer-Verlag Berlin Heidelberg

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Cui, X., Xiao, J., Chen, J., Sang, L. (2011). Improved Algorithm for Mining N-Most Interesting Itemsets. In: Deng, H., Miao, D., Wang, F.L., Lei, J. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2011. Communications in Computer and Information Science, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24282-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-24282-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24281-6

  • Online ISBN: 978-3-642-24282-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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