基于关联规则抽取的文本挖掘技术中的知识发现

V. Bhujade, N. Janwe
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引用次数: 19

摘要

本文描述了一种从文本文档集合中自动提取关联规则的文本挖掘技术。这种技术被称为从文本中提取关联规则(EART)。它依赖于关键字特征来发现标记文档的关键字之间的关联规则。EART系统忽略单词出现的顺序,而是关注单词及其在文档中的统计分布。该系统基于信息检索方案(TF-IDF)选择最重要的关键字进行关联规则生成。它包括三个阶段:文本预处理阶段(文档的转换、过滤、词根提取和索引),关联规则挖掘(ARM)阶段(应用我们设计的基于加权方案GARW的关联规则生成算法)和可视化阶段(结果的可视化)。与密码学相关的在线网页实验应用。提取的关联规则包含重要的特征。
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Knowledge Discovery in Text Mining Technique Using Association Rules Extraction
This paper describes text mining technique for automatically extracting association rules from collections of textual documents. The technique called, Extracting Association Rules from Text (EART). It depends on keyword features for discover association rules amongst keywords labeling the documents. EART system ignores the order in which the words occur, but instead focusing on the words and their statistical distributions in documents. The system based on Information Retrieval scheme (TF-IDF) for selecting most important keywords for association rules generation. It consists of three phases: Text Preprocessing phase (transformation, filtration, stemming and indexing of the documents), Association Rule Mining (ARM) phase (applying our designed algorithm for Generating Association Rules based on Weighting scheme GARW) and Visualization phase (visualization of results). Experiments applied on Online WebPages related to the cryptography. The extracted association rules contain important features.
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