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
- 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 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 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, 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
- 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 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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