Proceedings of the Third International Conference on Knowledge Discovery and Data Mining
Edited by David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy Uthurusamy
320 pp., references, index, illus., $75.00 softcover
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Advances in data gathering, storage, and distribution technologies have far outpaced our advances in techniques for helping humans analyze, understand, and digest this information. This has led to an all-too-common data glut situation creating a strong need and a valuable opportunity for extracting knowledge from databases. Both researchers and application developers have been responding to that need. Knowledge discovery in databases (KDD) and data mining are areas of common interest to researchers in machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. KDD applications have been developed for astronomy, biology, finance, insurance, marketing, medicine, and many other fields. The Third International Conference on Knowledge Discovery and Data Mining (KDD-97), held on August 14-17, 1997 in Newport Beach, California, USA, in conjunction with ASA-97, provided a forum for KDD researchers and practitioners to present their latest work. KDD-97 was a truly international conference. Of the 162 papers received for review, 43 percent came from outside the United States with the following distribution: Australia (2), Austria (1), Belgium (3), Brazil (1), Canada (4), China (4), Finland (2), France (5), Germany (9), Hong Kong (1), Israel (2), Japan (9), Korea (1), Mexico (1), Northern Ireland (1), Norway (1), Poland (1), Russia (2), Singapore (5), Slovenia (1), Spain (2), Sweden (1), Switzerland (1), Taiwan (3), The Netherlands (2), and UK (4). Only 17 of the submitted papers were accepted for presentation to the conference--an acceptance rate of 10 percent. In addition, 49 papers were accepted for poster presentation. The papers in this proceedings focus on core problems in KDD, such as representation issues, search complexity, the use of prior knowledge, statistical inference, algorithms for the analysis of massive amounts of data both in size and dimensionality, the use of domain knowledge, managing uncertainty, interactive (human-oriented) presentation, and applications.