Where is Knowledge?
Discovering Semantic Proximity using User Access
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
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
His research interests continue to be databases, artificial
intelligence, and computer graphics.