Fuzzy Rough Set Based Technique for User Specific Information Retrieval: A Case Study on Wikipedia Data

Nidhika Yadav, N. Chatterjee
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引用次数: 26

Abstract

Information retrieval is widely used due to extremely large volume of text and image data available on the web and consequently, efficient retrieval is required. Text information retrieval is a branch of information retrieval which deals with text documents. Another key factor is the concern for a retrieval engine, often referred to as user-specific information retrieval, which works according to a specific user. This article performs a preliminary investigation of the proposed fuzzy rough sets-based model for user-specific text information retrieval. The model improves on the computational time required to compute the approximations compared to classical fuzzy rough set model by using Wikipedia as the information source. The technique also improves on the accuracy of clustering obtained for user specified classes.
基于模糊粗糙集的用户特定信息检索技术:以维基百科数据为例
由于网络上的文本和图像数据量非常大,因此对信息检索的效率提出了更高的要求。文本信息检索是信息检索中处理文本文档的一个分支。另一个关键因素是对检索引擎的关注,通常称为特定于用户的信息检索,它根据特定用户工作。本文对提出的基于模糊粗糙集的用户特定文本信息检索模型进行了初步研究。该模型以维基百科为信息源,与经典模糊粗糙集模型相比,改进了计算近似所需的计算时间。该技术还提高了用户指定类的聚类精度。
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