信息过滤在Web数据挖掘中的应用

Xujuan Zhou, Yuefeng Li, P. Bruza, Sheng-Tang Wu, Yue Xu, Raymond Y. K. Lau
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引用次数: 26

摘要

随着Web信息量的迅速增长,提高Web信息检索的效率和准确性是一项艰巨的任务。关于Web信息收集的有效性,存在两个基本问题:信息不匹配和过载。为了解决这些难题,本文提出了一种集成信息过滤和复杂数据处理的模型。在该方案的第一阶段,在Web搜索过程中引入基于用户搜索意图的信息过滤器,快速过滤掉不相关的数据。在数据处理的第二阶段,利用简化后的数据建立模式分类模型(PTM)。PTM通过应用涉及更严格计算的数据挖掘技术使数据相关性合理化。实验结果表明,采用该方案可以实现更高效的Web信息访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Information Filtering in Web Data Mining Process
The amount of Web information is growing rapidly, improving the efficiency and accuracy of Web information retrieval is uphill battle. There are two fundamental issues regarding the effectiveness of Web information gathering: information mismatch and overload. To tackle these difficult issues, an integrated information filtering and sophisticated data processing model has been presented in this paper. In the first phase of the proposed scheme, an information filter that based on user search intents was incorporated in Web search process to quickly filter out irrelevant data. In the second data processing phase, a pattern taxonomy model (PTM) was carried out using the reduced data. PTM rationalizes the data relevance by applying data mining techniques that involves more rigorous computations. Several experiments have been conducted and the results show that more effective and efficient access Web information has been achieved using the new scheme.
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