Clustering and Identifying Temporal Trends in Document Databases

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.

• Type: Conference
• Year: 2000
• Where: ADL
• Authors: Alexandrin Popescul, Gary William Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles
• Keywords: ADL 2000, Digital Library, Efficient Method, Hyper-linked Document Databases, Temporal Trends