2000 ClusteringandIdentifyingTempora

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Subject Headings: Document Clustering, Temporal Modeling.

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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.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2000 ClusteringandIdentifyingTemporaAlexandrin Popescul
Lyle H. Ungar
Steve Lawrence
C. Lee Giles
Gary William Flake
Clustering and Identifying Temporal Trends in Document Databases