Where is Knowledge?
Discovering Semantic Proximity using User Access

Matthew Merzbacher
Mills College


Knowledge is everywhere, but is difficult to locate, isolate, and understand. After a discussion of different kinds of knowledge, we discuss our "Dynamic Nearness" data mining algorithm that detects semantic relationships between objects in a database, based on access patterns. The approach can be applied to web pages to allow automatic dynamic reconfiguration of a web site. We finish by experimentally answering some questions about the scalability of Dynamic Nearness.


Dr. Matthew Merzbacher went to Brown University as an undergraduate. After receiving his Sc.B. in Applied Math from Brown, he continued there and earned an Sc.M. in Computer Science in the area of computer graphics. In 1993, Dr. Merzbacher received his Ph.D. from UCLA in the area of cooperative database systems. Since then, he has taught at liberal arts colleges, currently holding an assistant professorship at Mills College in Oakland, where he also directs the graduate programs in Computer Science. His research interests continue to be databases, artificial intelligence, and computer graphics.