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  • 标题:Mining frequent item sets without candidate generation using FP-Trees
  • 本地全文:下载
  • 作者:G.Nageswara Rao ; Suman Kumar Gurram
  • 期刊名称:International Journal of Computer Science and Information Technologies
  • 电子版ISSN:0975-9646
  • 出版年度:2011
  • 卷号:2
  • 期号:6
  • 页码:2677-2685
  • 出版社:TechScience Publications
  • 摘要:There are so many algorithms for extracting frequent item sets.. These are very important for mining association rules as well as for many other data mining tasks . So many methods have been implemented for mining frequent item sets using a prefix tree structure known as frequent Pattern Tree (FP-Tree) for storing all the information about frequent item sets. In this paper we propose a new technique called fp-array technique based on FP-Tree Data structure, that reduces the traverse time of FPTrees and we can improve the performance of FP-Tree based algorithms. This FP-array technique will give the good results for sparse data sets. It consumes more memory when we use the sparse data sets , consumes less memory for dense data sets and the performance of this algorithm is very well when the minimum support is low
  • 关键词:frequent Pattern - Tree; frequent item sets;association rules.
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