Topic-driven Clustering for Document Datasets

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2005 SIAM International Conference on Data Mining (SDM)Topic-driven Clustering for Document DatasetsYing Zhao and George KarypisYing Zhao and George Karypispp.358 - 369Chapter DOI:https://doi.org/10.1137/1.9781611972757.32PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we define the problem of topic-driven clustering, which organizes a document collection according to a given set of topics. We propose three topic-driven schemes that consider the similarity between documents and topics and the relationship among documents themselves simultaneously. We present a comprehensive experimental evaluation of the proposed topic-driven schemes on five datasets. Our experimental results show that the proposed topic-driven schemes are efficient and effective with topic prototypes of different levels of specificity. Previous chapter Next chapter RelatedDetails Published:2005ISBN:978-0-89871-593-4eISBN:978-1-61197-275-7 https://doi.org/10.1137/1.9781611972757Book Series Name:ProceedingsBook Code:PR119Book Pages:xii + 648