节点文献

Mining Maximal Frequent Patterns in a Unidirectional FP-tree

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 宋晶晶刘瑞新王艳姜保庆

【Author】 SONG Jing-jing 1, LIU Rui-xin 2, WANG Yan 1,3, JIANG Bao-qing 11 Institute of Data and Knowledge Engineering, Henan University, Kaifeng 4750012 Information Engineering Science Department, Yellow River Conservancy Technical Institute, Kaifeng 4750033 Department of Computer Science and Application, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015

【机构】 Institute of Data and Knowledge Engineering Henan UniversityInformation Engineering Science DepartmentYellow River Conservancy Technical InstituteInstitute of Data and Knowledge EngineeringHenan UniversityKaifeng 475001Kaifeng 475003Kaifeng 475001 Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management Zhengzhou 450015

【摘要】 Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.

【Abstract】 Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.

【基金】 Supported by the National Natural Science Foundation of China ( No.60474022);Henan Innovation Project for University Prominent Research Talents (No.2007KYCX018)
  • 【文献出处】 Journal of DongHua University ,东华大学学报(英文版) , 编辑部邮箱 ,2006年06期
  • 【分类号】TP301.6
  • 【被引频次】4
  • 【下载频次】48
节点文献中: