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Web Content Outlier Framework for Enhancing Web Search Results through Mathematical Approaches

机译:Web内容异常值框架,用于通过数学方法增强Web搜索结果

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Nowadays, plenty, diverse and changing features of web persuades most of the users for seeking and retrieving the interesting information. On searching information on the web through the search engine, the unnecessary irrelevant and redundant information waste the user time and effort, lower the quality of web search results, increase indexing space and time complexity. So, it becomes an important issue to provide high quality and effective search results to retrieve information. Web content outliers mining studies on mining the insignificant and repetitive website pages from the remainder of other website pages under the ideal same class. Removal of content outliers from web enhances and upgrades the quality of web search results providing to the needs of the user. In this paper, the framework for web content outlier mining based on mathematical approaches is used. The experimental results show this framework provides better results in terms of Flmeasure and accuracy.
机译:如今,大量,多样化和不断变化的Web功能说服了大多数用户寻找和检索有趣的信息。通过搜索引擎在Web上搜索信息时,不必要的无关紧要的信息浪费了用户的时间和精力,降低了Web搜索结果的质量,增加了索引空间和时间复杂度。因此,提供高质量和有效的搜索结果以检索信息成为重要的问题。 Web内容离群值挖掘研究是从理想类别下的其他网站页面的其余部分中挖掘无关紧要的网站页面。从Web删除内容异常值可以增强和升级满足用户需求的Web搜索结果的质量。在本文中,使用了基于数学方法的Web内容离群值挖掘框架。实验结果表明,该框架在Flmeasure和准确性方面提供了更好的结果。

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