Association Mining on Massive Text under Full Confidence Based on Incremental Queue
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    Abstract:

    Association mining is an important data analysis method, this article proposes an incremental queue association mining algorithm model under full confidence,using the full confidence rules in the traditional FP-Growth and PF-Tree association mining algorithm can improve the algorithm adaptability. Thus, the article proposes FP4W-Growth algorithm, and applies this algotithm to the association calculation of text data and association mining of incremental data. Then this paper conducted verification experiment. The experimental results show the feasibility of this algorithm and model. The article provides a scientific approach to finding hidden but useful information and patterns from large amount of text data.

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刘炜.基于增量队列的在全置信度下的关联挖掘.计算机系统应用,2015,24(8):133-136

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History
  • Received:November 26,2014
  • Revised:January 19,2015
  • Adopted:
  • Online: September 03,2015
  • Published:
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