ACM Special Interest Group on Knowledge Discovery & Data Mining

KDD-2000

Sixth ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining
August 20-23, 2000
Boston, MA, USA

On Certain Rigorous Approaches to Data Mining

Christos H. Papadimitriou

C. Lester Hogan Professor and Associate Chair

Computer Science Division

EECS Department

University of California, Berkeley

Presentation (PDF, 219 KB)

Abstract

In a recent joint paper with Jon Kleinberg and Prabhakar Raghavan we proposed a novel formal approach to interestingness based on considerations from mathematical economics and optimization. Although this approach requires an understanding of the enterprise's business model and environment that is not realistically attainable, I shall argue that it can lead to interesting insights and novel styles of data mining. I will also discuss certain other foundational approaches to important current problems related to data mining, such as formalizing privacy, and sampling web documents uniformly at random.

Biography

Christos Papadimitriou has a bachelor from Athens Polytechnic and a PhD from Princeton. He has taught at Harvard, MIT, Athens Polytechnic, Stanford, and UCSD. Since 1995 he has been teaching at the University of California Berkeley, where he is the C. Lester Hogan Professor of Electrical Engineering and Computer Science. He has written five books, and over 200 articles on algorithms, complexity, and their applications to various fields, including databases, optimization, artificial intelligence, the life sciences, and economics.