计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于频繁2项集支持矩阵的Apriori改进算法

纪怀猛   

  1. (福州大学阳光学院,福州 350015)
  • 收稿日期:2012-10-16 出版日期:2013-11-15 发布日期:2013-11-13
  • 作者简介:纪怀猛(1976-),男,讲师、硕士,主研方向:人工智能,数据挖掘
  • 基金项目:
    福建省教育厅基金资助项目(JB12255)

Improved Apriori Algorithm Based on Frequency 2-item Set Support Matrix

JI Huai-meng   

  1. (Sunshine College, Fuzhou University, Fuzhou 350015, China)
  • Received:2012-10-16 Online:2013-11-15 Published:2013-11-13

摘要: Apriori算法在关联规则挖掘过程中需要多次扫描事务数据库,产生大量候选项目集,导致计算量过大。为解决该问题,提出一种基于频繁2项集支持矩阵的Apriori改进算法,通过分析频繁k+1项集的生成机制,将支持矩阵与频繁2项集矩阵相结合实现快速剪枝,并大幅减少频繁k项集验证的计算量。实验结果表明,与Apriori算法和ABTM算法相比,改进算法明显提高了频繁项集的挖掘效率。

关键词: 关联规则, 布尔矩阵, Apriori算法, 频繁项集, 支持矩阵

Abstract: As Apriori algorithm used for mining association rules can lead to a large number of candidate itemsets and huge computations, an improved Apriori algorithm based on frequency 2-item set support matrix is proposed. By analyzing the generation mechanism of frequent k+1 item sets, the improved algorithm combines assistant matrix and frequent 2-item matrix to realize rapid purning, it can trim infrequent item set quickly and reduce the amount of calculation of k frequent item set verification. Experimental result shows that frequent itemsets mining efficiency of improved algorithm increases significantly compared with Apriori algorithm and ABTM algorithm.

Key words: association rule, Boolean matrix, Apriori algorithm, frequent item set, support matrix

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