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关联规则挖掘算法在超市销售分析中的应用 被引量:12

Apply in the Sales Data Association Analysis with Association Analysis Data Mining Methodology
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摘要 销售数据分析是关联规则数据挖掘算法的主要应用领域之一,文章基于关联规则的算法理论,针对应用于超市销售关联规则的特点,提出了适用于超市销售相关性分析的模型。通过商业检验,该算法可以显著提高相关商品的销售额。 The sales data association analysis is one of the major application of the data mining methodology. Refering to the sales features of the retail points, the article sets up the retailing sales association analysis data model based on the association methodology. It proves that it can increase the sales dramatically by applying this methodology to the real business environment.
作者 唐敏
出处 《计算机科学》 CSCD 北大核心 2006年第2期149-150,共2页 Computer Science
关键词 数据挖掘 关联规则 APRIORI算法 销售分析 Data analysis,Association rule,Apriori methodology,Sales analysis
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参考文献6

  • 1Agrawal R,Imielinski T,Swami A.Mining Association RulesBetween Sets of Items in Large Databases [J].In:Proc.of ACM SIGMOD Conference on Management of Data,1993.207~216
  • 2Agrawal R,Srikant R.Fast Algorithms for Mining AssociationRules [J].In:Proc.of International Conference on VeryLarge Databases,1994.487~499
  • 3Han J,Pei J,Yin Y.Mining Frequent Patterns Without Candidate Generation.In:Proc.of ACM-SIGMOD Conference,2000.1~12
  • 4Wang K,Tang L,Han J,et al.Top Down FP-growth for Association Rule Mining.In:Proc.of PAKDD2002,2002.334~340
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二级参考文献10

  • 1Agrawal R, Imielinski T, Swami A. Mining Association Rules Between Sets of Items in Large Databases[J]. In:Proceedings of ACM SIGMOD Conference on Management of Data, 1993:207-216
  • 2Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules [J]. In: Proceedings of International Conference on Very Large Databases, 1994:487-499
  • 3Han J, Pei J, Yin Y. Mining Frequent Patterns Without Candidate Generation. In: Proc. ofACM-SIGMOD Conference, 2000: 1-12
  • 4Wang K, Tang L, Han J, et al. Top Down FP-growth for Association Rule Mining. In: Proceedings of PAKDD2002, 2002:334-340
  • 5Han J, JianYong, Lu Y, P, et al. Mining Top-K Frequent Closed Patterns Without Minimum Support. In: ICDM, 2002:211-218
  • 6Pei J, Han J, Lu H, et al. H-mine: Hyper-structure Mining of Frequent Patterns in Large Databases. In: ICDM, 2001:441-448
  • 7Zhou Z, Ezeife C I. A Low-scan Incrcmental Association Rule Maintenance Method Based on the Apriori Property. In: Canadian Conference on Al, 2001:26-35
  • 8Lin W, Alvarez S A, Ruiz C. Efficient Adaptive-support Association Rule Mining for Recommender Systems. In: Data Mining and Knowledge Discovery, 2002,6( 1 ): 83-105
  • 9Wang D, Bao Y, Yu G, et al. Using Page Classification and Association Rule Mining for Personalized Recommendation in Distance Learning. In: ICWL, 2002:363-376
  • 10Depaul CTI Web Usage Mining Data. http://maya.cs.depaul.edu/~classes/etc584/resource.html

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