| Study and Implementation of Web Mining Classification Algorithm Based on Building Tree of Detection Class Threshold |
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| 作者姓名： | 陈俊杰 宋瀚涛 陆玉昌 |
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| 作者单位： | [1]SchoolofInformationScienceandTechnology,BeijingInstituteofTechnology,Beijing100081,China [2]StateKeyLaboratoryofIntelligentTechnologyandSystem,TsinghuaUniversity,Beijing100084,China |
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| 基金项目： | 国家重点基础研究发展计划(973计划)，山西省自然科学基金 |
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| 摘 要： | A new classification algorithm for web mining is proposed on the basis of general classification algorithm for data mining in order to implement personalized information services. The building tree method of detecting class threshold is used for construction of decision tree according to the concept of user expectation so as to find classification rules in different layers. Compared with the traditional C4.5 algorithm, the disadvantage of excessive adaptation in C4.5 has been improved so that classification results not only have much higher accuracy but also statistic meaning.
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| 关 键 词： | 数据采集 分类算法 分类极限 互联网 |
| 收稿时间： | 2003/11/6 0:00:00 |
Study and Implementation of Web Mining Classification Algorithm Based on Building Tree of Detection Class Threshold |
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| CHEN Jun-jie,SONG Han-tao and LU Yu-chang.Study and Implementation of Web Mining Classification Algorithm Based on Building Tree of Detection Class Threshold[J].Journal of Beijing Institute of Technology,2005,14(2):126-129. |
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| Authors: | CHEN Jun-jie SONG Han-tao and LU Yu-chang |
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| Affiliation: | 1. School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2. State Key Laboratory of Intelligent Technology and System, Tsinghua University, Beijing 100084, China |
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| Abstract: | A new classification algorithm for web mining is proposed on the basis of general classification algorithm for data mining in order to implement personalized information services. The building tree method of detecting class threshold is used for construction of decision tree according to the concept of user expectation so as to find classification rules in different layers. Compared with the traditional C4. 5 algorithm, the disadvantage of excessive adaptation in C4. 5 has been improved so that classification results not only have much higher accuracy but also statistic meaning. |
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| Keywords: | data mining classification algorithm class threshold induced concept |
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