3. KDD 1997: Newport Beach, California, USA
David Heckerman, Heikki Mannila, Daryl Pregibon (Eds.): Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997. AAAI Press 1997 ISBN 1-57735-027-8
Plenary Papers
- Mark Derthick, John Kolojejchick, Steven F. Roth: An Interactive Visualization Environment for Data Exploration. 2-9
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu: Density-Connected Sets and their Application for Trend Detection in Spatial Databases. 10-15
- Ronen Feldman, Willi Klösgen, Amir Zilberstein: Visualization Techniques to Explore Data Mining Results for Document Collections. 16-23
- Eamonn J. Keogh, Padhraic Smyth: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. 24-30
- Gholamreza Nakhaeizadeh, Alexander Schnabl: Development of Multi-Criteria Metrics for Evaluation of Data Mining Algorithms. 37-42
- Foster J. Provost, Tom Fawcett: Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. 43-48
- Padhraic Smyth, Michael Ghil, Kayo Ide, Joseph Roden, Andrew Fraser: Detecting Atmospheric Regimes Using Cross-Validated Clustering. 61-66
- Ramakrishnan Srikant, Quoc Vu, Rakesh Agrawal: Mining Association Rules with Item Constraints. 67-73
- Salvatore J. Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, Dave W. Fan, Philip K. Chan: JAM: Java Agents for Meta-Learning over Distributed Databases. 74-81
- Ramesh Subramonian, Ramana Venkata, Joyce Chen: A Visual Interactive Framework for Attribute Discretization. 82-88
- Xiong Wang, Jason Tsong-Li Wang, Dennis Shasha, Bruce A. Shapiro, Sitaram Dikshitulu, Isidore Rigoutsos, Kaizhong Zhang: Automated Discovery of Active Motifs in Three Dimensional Molecules. 89-95
- Kunikazu Yoda, Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Computing Optimized Rectilinear Regions for Association Rules. 96-103
- Jan M. Zytkow: Knowledge = Concepts: A Harmful Equation. 104-109
KDD-97 Poster Papers
- Gediminas Adomavicius, Alexander Tuzhilin: Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach. 111-114
- Kamal Ali, Stefanos Manganaris, Ramakrishnan Srikant: Partial Classification Using Association Rules. 115-118
- John M. Aronis, Foster J. Provost: Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation. 119-122
- Roberto J. Bayardo Jr.: Brute-Force Mining of High-Confidence Classification Rules. 123-126
- Ulla Bergsten, Johan Schubert, Per Svensson: Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis. 127-130
- Christoph Breitner, Jörg Schlösser, Rüdiger Wirth: Process-Based Database Support for the Early Indicator Method. 131-134
- Jesús Cerquides, Ramon López de Mántaras: Proposal and Empirical Comparison of a Parallelizable Distance-Based Discretization Method. 139-142
- Jaturon Chattratichat, John Darlington, Moustafa Ghanem, Yike Guo, Harald Hüning, Martin Köhler, Janjao Sutiwaraphun, Hing Wing To, Dan Yang: Large Scale Data Mining: Challenges and Responses. 143-146
- Steve A. Chien, Forest Fisher, Helen Mortensen, Edisanter Lo, Ronald Greeley: Using Artificial Intelligence Planning to Automate Science Data Analysis for Large Image Databases. 147-150
- Dennis DeCoste: Mining Multivariate Time-Series Sensor Data to Discover Behavior Envelopes. 151-154
- Pedro Domingos: Why Does Bagging Work? A Bayesian Account and its Implications. 155-158
- Harris Drucker: Fast Committee Machines for Regression and Classification. 159-162
- Ronen Feldman, Yonatan Aumann, Amihood Amir, Amir Zilberstein, Willi Klösgen: Maximal Association Rules: A New Tool for Mining for Keyword Co-Occurrences in Document Collections. 167-170
- Gehad Galal, Diane J. Cook, Lawrence B. Holder: Improving Scalability in a Scientific Discovery System by Exploiting Parallelism. 171-174
- Ira J. Haimowitz, Özden Gür-Ali, Henry Schwarz: Integrating and Mining Distributed Customer Databases. 179-182
- Jukka Hekanaho: GA-Based Rule Enhancement in Concept Learning. 183-186
- Thomas H. Hinke, John A. Rushing, Heggere S. Ranganath, Sara J. Graves: Target-Independent Mining for Scientific Data: Capturing Transients and Trends for Phenomena Mining. 187-190
- David Jensen, Matthew D. Schmill: Adjusting for Multiple Comparisons in Decision Tree Pruning. 195-198
- Jonghyun Kahng, Wen-Hsiang Kevin Liao, Dennis McLeod: Mining Generalized Term Associations: Count Propagation Algorithm. 203-206
- Micheline Kamber, Jiawei Han, Jenny Chiang: Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes. 207-210
- Hillol Kargupta, Ilker Hamzaoglu, Brian Stafford: Scalable, Distributed Data Mining - An Agent Architecture. 211-214
- Alain Ketterlin: Clustering Sequences of Complex Objects. 215-218
- Stefan Kramer, Bernhard Pfahringer, Christoph Helma: Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail. 223-226
- Michael J. Pazzani, Subramani Mani, William Rodman Shankle: Beyond Concise and Colorful: Learning Intelligible Rules. 235-238
- Stephen Soderland: Learning to Extract Text-Based Information from the World Wide Web. 251-254
- Timothy M. Stough, Carla E. Brodley: Image Feature Reduction through Spoiling: Its Application to Multiple Matched Filters for Focus of Attention. 255-258
- Einoshin Suzuki: Autonomous Discovery of Reliable Exception Rules. 259-262
- Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, Sanjay Ranka: An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. 263-266
- Michael J. Turmon, Saleem Mukhtar, Judit Pap: Bayesian Inference for Identifying Solar Active Regions. 267-270
- Paul Xia: Knowledge Discovery in Integrated Call Centers: A Framework for Effective Customer-Driven Marketing. 279-282
- Mohammed Javeed Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li: New Algorithms for Fast Discovery of Association Rules. 283-286
- Oren Zamir, Oren Etzioni, Omid Madani, Richard M. Karp: Fast and Intuitive Clustering of Web Documents. 287-290
- Djamel A. Zighed, Ricco Rakotomalala, Fabien Feschet: Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning. 295-298
- Blaz Zupan, Marko Bohanec, Ivan Bratko, Bojan Cestnik: A Dataset Decomposition Approach to Data Mining and Machine Discovery. 299-302
Invited Talk
- Peter J. Huber: From Large to Huge: A Statistician's Reactions to KDD & DM. 304-308
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