基于用户辅助估计的相关网页搜索聚类

Clustering of Related Pages Based User-Assisted Estimation

随着web上的信息急剧增长,如何有效地从web上获得高质量的信息已经成为当今热门研究主题之一。在信息检索、数据挖掘、人工智能等领域,如何提高搜索信息结果的相似度,以提高搜索信息的质量,是众多研究的主要思考方法。文中在链接分析的基础上,基于SAHN分级聚类算法提出了以用户辅助估计进行相关网页的聚类搜索方法,与普通的聚类方法相比,实验通过比较三种常用的相似性聚类方法在提高搜索结果中的应用,发现结合用户辅助估计方法可以更好地提高搜索结果的满意度,达到更好的搜索效果。

With the rapid growth of the information on the web,how to effectively obtain high quality information has become one of today＇s hot topic in information retrieval,data mining,artificial intelligence,search for information on how to improve the results of similarity search for information in order to improve the quality of many of the major ways of thinking.In the link analysis based on hierarchical clustering algorithm based on SAHN proposed to estimate the associated auxiliary web users clustering search method,and the common clustering methods,experiments by comparing the similarity of the three commonly used clustering methods to improve the search results in the application of estimation methods that can be combined with user assistance to improve the search results to better satisfaction,to achieve better search results.