KDD Workshop 1994: Seattle, Washington, USA

Foundational Issues and Core Problems

• Ronald J. Brachman, Tej Anand: The Process of Knowledge Discovery in Databases: A First Sketch. 1-12
• Brian R. Gaines: Exception Dags as Knowledge Structures. 13-24
• Gregory Piatetsky-Shapiro, Christopher J. Matheus: The Interingness of Deviations. 25-36
• Evangelos Simoudis, Brian Livezey, Randy Kerber: Integrating Inductive and Deductive Reasoning for Database Mining. 37-48

Statistical and Probabilistic Models

• Robert St. Amant, Paul R. Cohen: Toward the Integration of Exploration and Modeling in a Planning Framework. 49-60
• Inderpal S. Bhandari, Shriram Biyani: On the Role of Statistical Significance in Exploratory Data Analysis. 61-72
• Dawn E. Gregory, Paul R. Cohen: Two Algorithms for Inducing Causal Models from Data. 73-84
• David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. 85-96
• Arno Siebes: Homogeneous Discoveries Contain No Surprises: Inferring Risk Profiles from Large Databases. 97-108
• Padhraic Smyth, Michael C. Burl, Usama M. Fayyad, Pietro Perona: Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth. 109-120
• Shusaku Tsumoto, Hiroshi Tanaka: Selection of Probabilistic Measure Estimation Method Based on Recursive Iteration of Resampling Methods. 121-132

Concept Discovery

• Wesley W. Chu, Kuorong Chiang: Abstraction of High Level Concepts from Numerical Values in Databases. 133-144
• Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. 145-156
• Jiawei Han, Yongjian Fu: Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases. 157-168
• Lawrence B. Holder, Diane J. Cook, Surnjani Djoko: Substucture Discovery in the SUBDUE System. 169-180
• Heikki Mannila, Hannu Toivonen, A. Inkeri Verkamo: Efficient Algorithms for Discovering Association Rules. 181-192
• Molly Troxel, Kim Swarm, Robert Zembowicz, Jan M. Zytkow: From Law-Like Knowledge to Concept Hierarchies in Data. 193-204
• Hiroshi Tsukimoto: The Discovery of Logical Propositions in Numerical Data. 205-216

Integrated and Interactive Systems

• Marcel Holsheimer, Martin L. Kersten: Architectural Support for Data Mining. 217-228
• Ibrahim F. Imam, Ryszard S. Michalski: From Facts to Rules to Decisions: An Overview of the FRD-1 System. 229-236
• Mikhail V. Kiselev: PolyAnalyst - A Machine Discovery System Inferring Functional Programs. 237-250
• Willi Klösgen: Exploration of Simulation Experiments by Discovery. 251-262
• Wim Van Laer, Luc Dehaspe, Luc De Raedt: Applications of a Logical Discovery Engine. 263-274
• Jean-Daniel Zucker, Vincent Corruble, J. Thomas, Geber Ramalho: DICE: A Discovery Environment Integrating Inductive Bias. 275-286

Database-Specific Techniques

• Sarabjot S. Anand, David A. Bell, John G. Hughes: Database Mining in the Architecture of a Semantic Preprocessor for State Aware Query Optimization. 287-298
• Roger H. L. Chiang, Terence M. Barron, Veda C. Storey: Extracting Domain Semantics for Knowledge Discovery in Relational Databases. 299-310
• Chun-Nan Hsu, Craig A. Knoblock: Rule Induction for Semantic Query Optimization. 311-322
• Xiaohua Hu, Nick Cercone, Jinshi Xie: Learning Data Trend Regularities From Databases in a Dynamic Environment. 323-334
• Wei-Min Shen, Bharat G. Mitbander, KayLiang Ong, Carlo Zaniolo: Using Metagueries to Integrate Inductive Learning and Deductive Database Technology. 335-346

Methodology and Application Issues

• John M. Aronis, Foster J. Provost: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning. 347-358
• Donald J. Berndt, James Clifford: Using Dynamic Time Warping to Find Patterns in Time Series. 359-370
• Johannes Fürnkranz: A Comparison of Pruning Methods for Relational Concept Learning. 371-382
• Kristian J. Hammond, Robin D. Burke, Kathryn Schmitt: A Case-Based Approach to Knowledge Navigation. 383-394
• T. J. Monk, R. S. Mitchell, L. A. Smith, G. Holmes: Geometric Comparison of Clarifications and Rule Sets. 395-406

Applications

• Chidanand Apté, Se June Hong: Predicting Equity Returns from Securities Data with Minimal Rule Generation. 407-418
• Marek J. Druzdze, Clark Glymour: Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges. 419-430
• Kenneth A. Kaufman: Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools. 431-440
• Christopher J. Matheus, Gregory Piatetsky-Shapiro, Dwight McNeill: An Application of KEFM to the Analysis of Healthcare Information. 441-452
• Raguram Sasisekharan, V. Seshadri, Sholom M. Weiss: Proactive Network Maintenance Using Machine Learning. 453-462

Machine Discovery Terminology

• Willi Klösgen, Jan M. Zytkow: Machine Discovery Terminology. 463-

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