内容相关性驱动的Web资源离群点挖掘技术研究与系统实现的中期报告.docx

内容相关性驱动的Web资源离群点挖掘技术研究与系统实现的中期报告 摘要： 随着Web资源的不断增加，如何快速、准确地挖掘出其中的离群点成为了一个亟待解决的问题。针对传统离群点挖掘方法在Web资源中的应用存在一定困难的问题，本文提出了一种内容相关性驱动的Web资源离群点挖掘技术。该方法结合了文本相似度和链接关系两方面信息，在资源的内容相关性和网络结构特征上进行了综合分析，能够精准地发现Web资源中的离群点。具体地，该方法首先利用Word2Vec算法对Web资源中的文本内容进行向量表示，计算文本相似度；然后，通过PageRank算法计算链接关系的影响力，并将其作为网络结构特征进行分析；最后，基于文本相似度和链接关系两方面的特征，利用孤立森林算法进行离群点检测。实验结果表明，该方法在Web资源离群点挖掘方面具有较高的准确性和效率。 关键词：Web资源；离群点挖掘；内容相关性；文本相似度；链接关系；孤立森林算法 Abstract: With the increasing number of Web resources, it is urgent to quickly and accurately mine outlier points. Considering the difficulties of traditional outlier mining methods in web resources application, this paper proposes a content-related-driven web resource outlier mining technology. This method combines information on text similarity and link relationship and comprehensively analyzes the content correlation and network structure characteristics of resources, which can accurately discover the outlier points in web resources. Specifically, this method first uses the Word2Vec algorithm to vectorially represent the text content in web resources and calculate text similarity. Then, the PageRank algorithm calculates the influence of link relationship and analyzes it as network structure characteristics. Finally, based on the features of text similarity and link relationship, we use the isolation forest algorithm for outlier detection. Experimental results show that the proposed method has high accuracy and efficiency in outlier mining of web resources. Keywords: Web resources; outlier mining; content relevance; text similarity; link relationship; isolation forest algorithm