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| 基于多粒度树模型的Web站点描述及挖掘算法 |
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田永鸿1, 黄铁军1,2, 高文1,2,3
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1.中国科学院,计算技术研究所,北京,100080;2.中国科学院,研究生院,北京,100039;3.哈尔滨工业大学,计算机科学与工程系,黑龙江,哈尔滨,150001
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| 摘要: |
| 随着Web所拥有的信息量和信息种类的急剧增长,Web站点挖掘对于自动实现特定主题的Web资源发现和分类具有重要的意义.然而现有的Web站点分类或挖掘算法在利用上下文语义信息、去除噪声信息以进一步提高分类准确率等方面还缺乏深入研究.从站点的采样尺寸、分析粒度和描述结构3个方面分析了设计高效的Web站点挖掘算法所需要解决的问题.在此基础上,提出了一种新的Web站点多粒度树描述模型,并描述了包括基于隐Markov树的两阶段分类算法、粒度间上下文融合算法、两阶段去噪程序以及基于熵的动态剪枝策略在内的多粒度Web站点挖掘算法.站点的多粒度描述方法及挖掘算法为多站点查询优化、Web效用挖掘等的深入研究奠定了基础.实验表明,该算法相对于基线系统平均可以提高16%的分类准确率,并减少了34.5%的处理时间. |
| 关键词: 算法 Web站点挖掘 多粒度站点树 上下文模型 隐Markov树 多粒度分类 基于熵的剪枝 |
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| 基金项目:Supported by the "Knowledge Innovation Initiative" of the Chinese Academy of Sciences under Grant No.Kgcxz-103(中国科学院知识创新工程) |
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| A Web Site Representation and Mining Algorithm Using the Multiscale Tree Model |
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TIAN Yong-Hong,HUANG Tie-Jun,GAO Wen
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| Abstract: |
| With the exponential growth of both the amount and the diversity of the web information, web site mining is highly desirable for automatically discovering and classifying topic-specific web resources from the World Wide Web. Nevertheless, existing web site mining methods have not yet handled adequately how to make use of all the correlative contextual semantic clues and how to denoise the content of web sites effectually so as to obtain a better classification accuracy. This paper circumstantiates three issues to be solved for designing an effective and efficient web site mining algorithm, i.e., the sampling size, the analysis granularity, and the representation structure of web sites. On the basis, this paper proposes a novel multiscale tree representation model of web sites, and presents a multiscale web site mining approach that contains an HMT-based two-phase classification algorithm, a context-based interscale fusion algorithm, a two-stage text-based denoising procedure, and an entropy-base pruning strategy. The proposed model and algorithms may be used as a starting-point for further investigating some related issues of web sites, such as query optimization of multiple sites and web usage mining. Experiments also show that the approach achieves in average 16% improvement in classification accuracy and 34.5% reduction in processing time over the baseline system. |
| Key words: algorithm Web site mining multiscale site tree context model hidden Markov tree (HMT) multiscale classification entropy-based pruning |
