Efficiently mining frequent itemsets with compact FP-tree

  • Authors:

  • Qin Liang-Xi;Luo Ping;Shi Zhong-Zhi

  • Affiliations:

  • Key Lab of Intell. Info. Proc., Inst. of Comp. Technol., Ch. Acad. of Sci., Beijing and Grad. Sch. of Ch. Acad. of Sci., Beijing and Coll. of Comp. and Info. Eng., Guangxi Univ., Nanning;Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing and Graduate School of Chinese Academy of Sciences, Beijing;Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing

  • Venue:
  • Intelligent information processing II
  • Year:
  • 2004

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Abstract

FP-growth algorithm is an efficient algorithm for mining frequent patterns. It scans database only twice and does not need to generate and test the candidate sets that is quite time consuming. The efficiency of the FP-growth algorithm outperforms previously developed algorithms. But, it must recursively generate huge number of conditional FP-trees that requires much more memory and costs more time.In this paper, we present an algorithm, CFPmine, that is inspired by several previous works. CFPmine algorithm combines several advantages of existing techniques. One is using constrained subtrees of a compact FP-tree to mine frequent pattern, so that it is doesn't need to construct conditional FP-trees in the mining process. Second is using an array-based technique to reduce the traverse time to the CFP-tree. And an unified memeory management is also implemented in the algorithm. The experimental evaluation shows that CFPmine algorithm is a high performance algorithm. It outperforms Apriori, Eclat and FP-growth and requires less memory than FP-growth.