Knowledge Discovery in Databases
Papers from the AAAI Workshop
Usama M. Fayyad and Ramasamy Uthurusamy Program Cochairs
Technical Report WS-94-03 published by The AAAI Press, Menlo Park, California
This technical report is also available in book and CD format.
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Contents
Part I. Foundational Issues and Core Problems
Preface / 1
Usama M. Fayyad and Ramasamy Uthurusamy
The Process of Knowledge Discovery in Databases: A First Sketch / 1
Ronald J. Brachman and Tej Anand
Exception Dags as Knowledge Structures / 13
Brian R. Gaines
The Interingness of Deviations / 25
Gregory Piatetsky-Shapiro and Christopher J. Matheus
Integrating Inductive and Deductive Reasoning for Database Mining / 37
Evangelos Simoudis, Brian Livezey, and Randy Kerber
Part II. Statistical and Probabilistic Models
Toward the Integration of Exploration and Modeling in a Planning Framework / 49
Robert St. Amant and Paul R. Cohen
On the Role of Statistical Significance in Exploratory Data Analysis / 61
Inderpal Bhandari and Shriram Biyani
Two Algorithms for Inducing Causal Models from Data / 73
Dawn E. Gregory and Paul R. Cohen
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data / 85
David Heckerman, Dan Geiger, and David M. Chickering
Homogeneous Discoveries Contain No Surprises: Inferring Risk Profiles from Large Databases / 97
Arno Siebes
Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth / 109
Padhraic Smyth, Michael Burl, Usama Fayyad, and Pietro Perona
Selection of Probabilistic Measure Estimation Method Based on Recursive Iteration of Resampling Methods / 121
Shusaku Tsumoto and Hiroshi Tanaka
Part III. Concept Discovery
Abstraction of High Level Concepts from Numerical Values in Databases / 133
Wesley W. Chu and Kuorong Chiang
Discovering Informative Patterns and Data Cleaning / 145
Isabelle Guyon, N. Matic, and V. Vapnik
Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases / 157
Jiawei Han and YongJian Fu
Substucture Discovery in the SUBDUE System / 169
Lawrence B. Holder, Diane J. Cook, and Surnjani Djoko
Efficient Algorithms for Discovering Association Rules / 181
Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkarno
From Law-Like Knowledge to Concept Hierarchies in Data / 193
Molly Troxel, Kim Swarm, Robert Zembowicz, and Jan M. Zytkow
The Discovery of Logical Propositions in Numerical Data / 205
Hiroshi Tsukimoto
Part IV. Integrated and Interactive Systems
Architectural Support for Data Mining / 217
Marcel Holsheimer and Martin L. Kersten
From Facts to Rules to Decisions: An Overview of the FRD-1 System/ 229
Ibrahim F. Imam and Ryszard S. Michalski
PolyAnalyst-A Machine Discovery System Inferring Functional Programs / 237
Mikhail V. Kiselev
Exploration of Simulation Experiments by Discovery / 251
Willi Klosgen
Applications of a Logical Discovery Engine/ 263
Wim VanLaer, Luc Dehaspe, Luc DeRaedt
DICE: a Discovery Environment Integrating Inductive Bias / 275
J. Zucker, V. Corruble, J. Thomas, and G. Ramalho
Part V. Database-Specific Techniques
Database Mining in the Architecture of a Semantic Preprocessor for State Aware Query Optimization / 287
Sarabjot S. Anand, David A. Bell, and John G. Hughes
Extracting Domain Semantics for Knowledge Discovery in Relational Databases / 299
Roger H. L. Chiang, Terence M. Barron, and Veda C. Storey
Rule Induction for Semantic Query Optimization / 311
Chun-Nan Hsu and Craig A. Knoblock
Learning Data Trend Regularities From Databases in a Dynamic Environment / 323
Xiaohua Hu, Nick Cercone, and Jinshi Xie
Using Metagueries to Integrate Inductive Learning and Deductive Database Technology / 335
W. Shen, B. Mitbander, and KayLiang Ong, and C. Zaniolo
Part VI. Methodology and Application Issues
Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning / 347
John M. Aronis and Foster J. Provost
Using Dynamic Time Warping to Find Patterns in Time Series / 359
Donald J. Berndt and James Clifford
A Comparison of Pruning Methods for Relational Concept Learning / 371
Johannes Furnkranz
A Case-Based Approach to Knowledge Navigation / 383
Kristian J. Hammond, Robin Burke, and Kathryn Schmitt
Geometric Comparison of Clarifications and Rule Sets / 395
T. J. Monk, R. S. Mitchell, L.A. Smith, and G. Holmes
Part VII. Applications
Predicting Equity Returns from Securities Data with Minimal Rule Generation / 407
Chidanand Apte and Se June Hon
Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges / 419
Marek J. Druzdze and Clark Glymour
Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools / 431
Kenneth A. Kaufman
An Application of KEFM to the Analysis of Healthcare Information / 441
Christopher J. Matheus, Gregory Piatetsky-Shapiro, and Dwight McNeill
Proactive Network Maintenance Using Machine Learning / 453
R. Sasisekharan, V. Seshadri, and S. M. Weiss
Part VIII. Machine Discovery Terminology
Machine Discovery Terminology / 463
Willi Klosge and Jan M. Zytkow
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