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 language||English (US)|
|Number of pages||10|
|Journal||Proceedings of the Forum on Research and Technology Advances in Digital Libraries, ADL|
|State||Published - Jan 1 2000|
|Event||ADL 2000: IEEE Advances in Digital Libraries - Washington, DC, USA|
Duration: May 22 2000 → May 24 2000
All Science Journal Classification (ASJC) codes