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3rd KDD 1997: Newport Beach, California, USA
- David Heckerman, Heikki Mannila, Daryl Pregibon:
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 - Bing Liu, Wynne Hsu, Shu Chen:
Using General Impressions to Analyze Discovered Classification Rules. 31-36 - 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 - Y. Dan Rubinstein, Trevor Hastie:
Discriminative vs Informative Learning. 49-53 - Padhraic Smyth, David H. Wolpert:
Anytime Exploratory Data Analysis for Massive Data Sets. 54-60 - 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 E. 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 - Clifford Brunk, James Kelly, Ron Kohavi:
MineSet: An Integrated System for Data Mining. 135-138 - Jesús Cerquides, Ramón 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 Frank 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 M. Domingos:
Why Does Bagging Work? A Bayesian Account and its Implications. 155-158 - Harris Drucker:
Fast Committee Machines for Regression and Classification. 159-162 - Robert Engels, Guido Lindner, Rudi Studer:
A Guided Tour through the Data Mining Jungle. 163-166 - 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 - Udo Hahn, Klemens Schnattinger:
Deep Knowledge Discovery from Natural Language Texts. 175-178 - 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 - K. M. Ho, Paul D. Scott:
Zeta: A Global Method for Discretization of Continuous Variables. 191-194 - David D. Jensen, Matthew D. Schmill:
Adjusting for Multiple Comparisons in Decision Tree Pruning. 195-198 - George H. John, Brian Lent:
SIPping from the Data Firehose. 199-202 - 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 - Edwin M. Knorr, Raymond T. Ng:
A Unified Notion of Outliers: Properties and Computation. 219-222 - Stefan Kramer, Bernhard Pfahringer, Christoph Helma:
Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail. 223-226 - Brian Lent, Rakesh Agrawal, Ramakrishnan Srikant:
Discovering Trends in Text Databases. 227-230 - Ted Mihalisin, John Timlin:
Fast Robust Visual Data Mining. 231-234 - Michael J. Pazzani, Subramani Mani, William Rodman Shankle:
Beyond Concise and Colorful: Learning Intelligible Rules. 235-238 - Foster J. Provost, Venkateswarlu Kolluri:
Scaling Up Inductive Algorithms: An Overview. 239-242 - J. Sunil Rao, William J. E. Potts:
Visualizing Bagged Decision Trees. 243-246 - Arno Siebes, Martin L. Kersten:
KESO: Minimizing Database Interaction. 247-250 - 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 - Ke Wang, Huiqing Liu:
Schema Discovery for Semistructured Data. 271-274 - Ke Wang, Suman Sundaresh:
Selecting Features by Vertical Compactness of Data. 275-278 - 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 - Ning Zhong, Chunnian Liu, Yoshitsugu Kakemoto, Setsuo Ohsuga:
KDD Process Planning. 291-294 - 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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