| Discovering Frequent Subtrees from XML Data Using Neural Networks |
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| 作者姓名： | SUN Wei LIU Da-xin WANG Tong |
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| 作者单位： | College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, Heilongjiang, China |
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| 基金项目： | Supported by Key Science-Technology Project of HeilonRiiang Province（ GA010401-3） |
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| 摘 要： | By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML has been available on Internet. In this paper, we study a data-mining problem of discovering frequent ordered sub-trees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm of Ordered Subtree Miner （OSTMiner） based on two- layer neural networks with Hebb rule, that computes all ordered sub-trees appearing in a collection of XML trees with frequent above a user-specified threshold using a special structure EM-tree. In this algo- rithm, EM-tree is used as an extended merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. Experiments results showed that OSTMiner has good response time and scales well.
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| 关 键 词： | 数据挖掘 神经网络 XML 频率子树 |
| 文章编号： | 1007-1202（2006）01-0117-05 |
| 收稿时间： | 2005-05-28 |
Discovering frequent subtrees from XML data using neural networks |
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| SUN Wei LIU Da-xin WANG Tong.Discovering Frequent Subtrees from XML Data Using Neural Networks[J].Wuhan University Journal of Natural Sciences,2006,11(1):117-121. |
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| Authors: | Sun Wei Liu Da-xin Wang Tong |
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| Institution: | (1) College of Computer Science and Technology, Harbin Engineering University, 150001 Harbin, Heilongjiang, China |
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| Abstract: | By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML has been available on Internet. In this paper, we study a data-mining problem of discovering frequent ordered sub-trees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm of Ordered Subtree Miner(OSTMiner) based on two- layer neural networks with Hebb rule, that computes all ordered sub-trees appearing in a collection of XML trees with frequent above a user-specified threshold using a special structure EM-tree. In this algorithm, EM-tree is used as an extended merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. Experiments results showed that OSTMiner has good response time and scales well. |
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| Keywords: | XML frequent subtrees data mining neural networks |
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