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  The poster sessions will all be held in the California Ballroom at the Newport Beach Marriott Hotel and Tennis Club, Newport Beach, California, Friday, August 15, 1997, from 3.00pm to 7.00pm. |
 Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach Gediminas Adomavicius, New York University and Alexander Tuzhilin, Stern School of Business, New York University Partial Classification Using Association Rules Kamal Ali and Stefanos Manganaris, IBM Global Business Intelligence Solutions; Ramakrishnan Srikant, IBM Almaden Research Center Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation John M. Aronis, University of Pittsburgh and Foster J. Provost, NYNEX Science and Technology Brute-Force Mining of High-Confidence Classification Rules Roberto J. Bayardo, Jr., The University of Texas at Austin Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis Ulla Bergsten, Johan Schubert, and Per Svensson, Defence Research Establishment, Sweden Process-Based Database Support for the Early Indicator Method Christoph Breitner and Jörg Schlösser, University of Karlsruhe, Germany; Rüdiger Wirth, Daimler-Benz AG, Germany MineSet: An Integrated System for Data Mining Cliff Brunk, James Kelly, and Ron Kohavi, Silicon Graphics, Inc. Proposal and Empirical Comparison of a Parallelizable Distance-Based Discretization Method Jesús Cerquides and Ramon López de Màntaras, Spanish Council for Scientific Research, Spain Large Scale Data Mining: Challenges and Responses Jaturon Chattratichat, John Darlington, Moustafa Ghanem, Yike Guo, Harald Hüning, Martin Köhler, Janjao Sutiwaraphun, Hing Wing To, and Dan Yang, Imperial College of London, United Kingdom Using Artificial Intelligence Planning to Automate Science Data Analysis for Large Image Databases Steve Chien, Forest Fisher, and Helen Mortensen, Jet Propulsion Laboratory, California Institute of Technology; Edisanter Lo and Ronald Greeley, Arizona State University Mining Multivariate Time-Series Sensor Data to Discover Behavior Envelopes Dennis DeCoste, Jet Propulsion Laboratory, California Institute of Technology Why Does Bagging Work? A Bayesian Account and its Implications Pedro Domingos, University of California, Irvine Fast Committee Machines for Regression and Classification Harris Drucker, Monmouth University A Guided Tour through the Data Mining Jungle Robert Engels, University of Karlsruhe, Germany; Guido Lindner, Daimler Benz AG, Germany; and Rudi Studer, University of Karlsruhe, Germany Maximal Association Rules: A New Tool for Mining for Keyword Co-occurrences in Document Collections Ronen Feldman, Yonatan Aumann, Amihood Amir, and Amir Zilberstein, Bar-Ilan University, Israel; Willi Kloesgen, German National Research Center for Information Technology, Germany Improving Scalability in a Scientific Discovery System by Exploiting Parallelism Gehad Galal, Diane J. Cook, and Lawrence B. Holder, University of Texas at Arlington |
 Deep Knowledge Discovery from Natural Language Texts Udo Hahn and Klemens Schnattinger, Freiburg University, Germany Integrating and Mining Distributed Customer Databases Ira J. Haimowitz, Özden Gür-Ali, and Henry Schwarz, General Electric Corporate Research and Development GA-Based Rule Enhancement in Concept Learning Jukka Hekanaho, Turku Center for Computer Science and Åbo Akademi University, Finland Target-Independent Mining for Scientific Data: Capturing Transients and Trends for Phenomena Mining Thomas H. Hinke, John Rushing, Heggere Ranganath, and Sara J. Graves, University of Alabama in Huntsville Zeta: A Global Method for Discretization of Continuous Variables K. M. Ho and P. D. Scott, University of Essex, United Kingdom Adjusting for Multiple Comparisons in Decision Tree Pruning David Jensen and Matt Schmill, University of Massachusetts, Amherst SIPping from the Data Firehose George H. John, IBM Almaden Research Center and Brian Lent, Stanford University Mining Generalized Term Associations: Count Propagation Algorithm Jonghyun Kahng, Wen-Hsiang Kevin Liao, and Dennis McLeod, University of Southern California Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes Micheline Kamber, Jiawei Han, and Jenny Y. Chiang, Simon Fraser University, Canada Scalable, Distributed Data Mining-An Agent Architecture Hillol Kargupta, Ilker Hamzaoglu, and Brian Stafford, Los Alamos National Laboratory Clustering Sequences of Complex Objects A. Ketterlin, LSIIT, France A Unified Notion of Outliers: Properties and Computation Edwin M. Knorr and Raymond T. Ng, University of British Columbia, Canada Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail Stefan Kramer, Austrian Research Institute for Artificial Intelligence, Austria; Bernhard Pfahringer, University of Waikato, New Zealand; and Christoph Helma, University of Vienna, Austria Trellis Graphics Displays: A Multi-Dimensional Data Visualization Tool for Data Mining R. Douglas Martin, MathSoft, Inc. Fast Robust Visual Data Mining Ted Mihalisin, Temple University and John Timlin, Mihalisin Associates, Inc. |
 Beyond Concise and Colorful: Learning Intelligible Rules Michael J. Pazzani, Subramani Mani, and W. Rodman Shankle, The University of California, Irvine Scaling Up Inductive Algorithms: An Overview Foster Provost, NYNEX Science and Technology and Venkateswarlu Kolluri, University of Pittsburgh Visualizing Bagged Decision Trees J. Sunil Rao, Cleveland Clinic and William J.E. Potts, SAS Institute Inc. KESO: Minimizing Database Interaction Arno Siebes and Martin L. Kersten, CWI, The Netherlands Learning to Extract Text-Based Information from the World Wide Web Stephen Soderland, University of Washington Image Feature Reduction through Spoiling: Its Application to Multiple Matched Filters for Focus of Attention Timothy M. Stough and Carla E. Brodley, Purdue University Autonomous Discovery of Reliable Exception Rules Einoshin Suzuki, Yokohama National University, Japan An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, and Sanjay Ranka, University of Florida Bayesian Inference for Identifying Solar Active Regions Michael Turmon and Saleem Mukhtar, Jet Propulsion Laboratory, California Institute of Technology; Judit Pap, University of California, Los Angeles Schema Discovery for Semistructured Data Ke Wang and Huiqing Liu, National University of Singapore, Singapore Selecting Features by Vertical Compactness of Data Ke Wang and Suman Sundaresh, National University of Singapore, Singapore Knowledge Discovery in Integrated Call Centers: A Framework for Effective Customer-Driven Marketing Paul Xia, EIS International Inc. New Algorithms for Fast Discovery of Association Rules M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, University of Rochester Fast and Intuitive Clustering of Web Documents Oren Zamir, Oren Etzioni, Omid Madani, and Richard M. Karp, University of Washington KDD Process Planning Ning Zhong, Yamaguchi University, Japan; Chunnian Liu, Beijing Polytechnic University, China; Yoshitsugu Kakemoto, The University of Tokyo, Japan; Setsuo Ohsuga, Waseda University, Japan Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning D. A. Zighed, R. Rakotomalala, and F. Feschet, University of Lyon 2, France A Dataset Decomposition Approach to Data Mining and Machine Discovery Blaz Zupan and Marko Bohanec, Institute Jozef Stefan, Slovenia; Ivan Bratko, Institute Jozef Stefan and University of Ljubljana, Slovenia; Bojan Cestnik, Temida and Institute Jozef Stefan, Slovenia |
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