Formal concept analysis support for web document clustering based on social tagging

Abstract

Web document clustering is one of the most important research branches of Clustering Analyzing. The objective of web document clustering is to meet the need of retrieving web document efficiently from massive information in Internet. Recently social tagging is the important form of document organization in web 2.0, and the tagging as a document descriptor is used to improve the effectiveness of web searching. But a web document usually belongs to various category of tagging, which may lead to the difficulty of browsing web document based on single tagging. This paper explores the use of Formal Concept Analysis (FCA) as mathematical tool to analyze the social tagging of web document, and presents a model for web document clustering based on tagging semantic. Furthermore, taking community web site Douban as an example, the model is applied to allow users to tag and serendipitously browse web document using Formal Concept Analysis.