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  • 标题:Mining Efficient Association Rules Through Apriori Algorithm Using Attributes
  • 本地全文:下载
  • 作者:Mamta Dhanda ; Sonali Guglani ; Gaurav Gupta
  • 期刊名称:International Journal of Computer Science & Technology
  • 印刷版ISSN:2229-4333
  • 电子版ISSN:0976-8491
  • 出版年度:2011
  • 卷号:2
  • 期号:3(Version 2)
  • 出版社:Ayushmaan Technologies
  • 摘要:In data mining a number of algorithms has been proposed. Each algorithm has a different objective. A lot of research has been done on these various data mining fields and algorithms. Extraction of valuable data from large dataset is an emerging problem. Apriori algorithm is the algorithm to extract association rules from dataset. Apriori algorithm is not an efficient algorithm as it is a time consuming algorithm in case of large dataset. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. This paper illustrate the apriori algorithm disadvantages and utilization of attributes which can improve the efficiency of apriori algorithm. system.
  • 关键词:frequent itemset ; Apriori ; profit; quantity; support.
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