| A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining |
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| 作者姓名： | DONG Yihong ZHUANG Yueting TAI Xiaoying |
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| 作者单位： | [1]Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, Zhejiang, China [2]Institute of Information Science and Engineering, Ningbo University, Ningbo 315211,Zhejiang, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China（60472099） and Ningbo Natural Science Foundation（2006A610017） |
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| 摘 要： | Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by ＂pruning and laying back＂ to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.
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| 关 键 词： | 增加算法 数据挖掘 模式树 数据库 |
| 文章编号： | 1007-1202（2007）05-0777-06 |
| 收稿时间： | 24 January 2007 |
| 修稿时间： | 2007-01-24 |
A novel incremental mining algorithm of frequent patterns for web usage mining |
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| DONG Yihong ZHUANG Yueting TAI Xiaoying.A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining[J].Wuhan University Journal of Natural Sciences,2007,12(5):777-782. |
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| Authors: | Dong Yihong Zhuang Yueting Tai Xiaoying |
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| Institution: | (1) Institute of Artificial Intelligence, Zhejiang University, Hangzhou, 310027, Zhejiang, China;(2) Institute of Information Science and Engineering, Ningbo University, Ningbo, 315211, Zhejiang, China |
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| Abstract: | Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable.
In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for
global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically
in inserting process by “pruning and laying back” to keep the frequency descending order so that they can be shared to approaching
optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The
results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient
than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer
in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.
Biography: DONG Yihong(1969–), male, Ph. D. candidate, Associate professor in Ningbo University, research direction: data mining, artificial
intelligence and neural networks. |
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| Keywords: | incremental algorithm association rule frequent pattern tree web usage mining |
| 本文献已被 维普 SpringerLink 等数据库收录！ |
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