|
|||||
|
|
| Mining Maximal Frequent Patterns in a Unidirectional FP-tree | |
| 作者姓名： | 宋晶晶 刘瑞新 王艳 姜保庆 |
| 作者单位： | [1]Institute of Data and Knowledge Engineering, Henan University, Kaifeng 475001 [2]Information Engineering Science Department, Yellow River Conservancy Technical Institute, Kaifeng 475003 [3]Department of Computer Science and Application, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015 |
| 基金项目： | 国家自然科学基金;河南省高校杰出科研创新人才工程项目 |
| 摘 要： | Becausemining 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. |
| 关 键 词： | 数据库 计算机技术 数据处理 信息技术 数据挖掘 |
| 收稿时间： | 2006-08-20 |
Mining Maximal Frequent Patterns in a Unidirectional FP-tree |
|
| SONG Jing-jing,LIU Rui-xin,WANG Yan,JIANG Bao-qing.Mining Maximal Frequent Patterns in a Unidirectional FP-tree[J].Journal of Donghua University,2006,23(6):105-109. | |
| Authors: | SONG Jing-jing LIU Rui-xin WANG Yan JIANG Bao-qing |
| Institution: | 1. Institute of Data and Knowledge Engineering, Henan University, Kaifeng 475001 2. Information Engineering Science Department, Yellow River Conservancy Technical Institute, Kaifeng 475003 3. Institute of Data and Knowledge Engineering, Henan University, Kaifeng 475001;Department of Computer Science and Application, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015 |
| 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. |
| Keywords: | data mining frequent pattern the maximal frequent pattern Unid _ FP-tree conditional Unid _ FP-tree |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |