Clustering and identifying temporal trends in document databases

Alexandrin Popescul, Gary William Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles

Research output: Contribution to journalConference article

59 Scopus citations

Abstract

We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying regularity often found in hyper-linked document databases. Because of this scalability, we can use our method to study the temporal trends of individual clusters in a statistically meaningful manner. As an example of our approach, we give a summary of the temporal trends found in a scientific literature database with thousands of documents.

Original languageEnglish (US)
Pages (from-to)173-182
Number of pages10
JournalProceedings of the Forum on Research and Technology Advances in Digital Libraries, ADL
StatePublished - Jan 1 2000
EventADL 2000: IEEE Advances in Digital Libraries - Washington, DC, USA
Duration: May 22 2000May 24 2000

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

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