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Home / Proceedings / AAAI Workshop Papers 1994 /

Knowledge Discovery in Databases

Foundational Issues and Core Problems

  • The Process of Knowledge Discovery in Databases: A First Sketch

    Ronald J. Brachman, Tej Anand

    1

    PDF
  • Exception Dags as Knowledge Structures

    Brian R. Gaines

    13

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  • The Interingness of Deviations

    Gregory Piatetsky-Shapiro, Christopher J. Matheus

    25

    PDF
  • Integrating Inductive and Deductive Reasoning for Database Mining

    Evangelos Simoudis, Brian Livezey, and Randy Kerber

    37

    PDF

Contents

  • Preface

    Usama M. Fayyad, Ramasamy Uthurusamy

    00

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Statistical and Probabilistic Models

  • Toward the Integration of Exploration and Modeling in a Planning Framework

    Robert St. Amant, Paul R. Cohen

    49

    PDF
  • On the Role of Statistical Significance in Exploratory Data Analysis

    Inderpal Bhandari, Shriram Biyani

    61

    PDF
  • Two Algorithms for Inducing Causal Models from Data

    Dawn E. Gregory, Paul R. Cohen

    73

    PDF
  • Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

    David Heckerman, Dan Geiger, and David M. Chickering

    85

    PDF
  • Homogeneous Discoveries Contain No Surprises: Inferring Risk Profiles from Large Databases

    Arno Siebes

    97

    PDF
  • Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth

    Padhraic Smyth, Michael Burl, Usama Fayyad, and Pietro Perona

    109

    PDF
  • Selection of Probabilistic Measure Estimation Method Based on Recursive Iteration of Resampling Methods

    Shusaku Tsumoto, Hiroshi Tanaka

    121

    PDF

Concept Discovery

  • Abstraction of High Level Concepts from Numerical Values in Databases

    Wesley W. Chu, Kuorong Chiang

    133

    PDF
  • Discovering Informative Patterns and Data Cleaning

    Isabelle Guyon, N. Matic, and V. Vapnik

    145

    PDF
  • Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases

    Jiawei Han, YongJian Fu

    157

    PDF
  • Substucture Discovery in the SUBDUE System

    Lawrence B. Holder, Diane J. Cook, and Surnjani Djoko

    169

    PDF
  • Efficient Algorithms for Discovering Association Rules

    Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkarno

    181

    PDF
  • From Law-Like Knowledge to Concept Hierarchies in Data

    Molly Troxel, Kim Swarm, Robert Zembowicz, and Jan M. Zytkow

    193

    PDF
  • The Discovery of Logical Propositions in Numerical Data

    Hiroshi Tsukimoto

    205

    PDF

Integrated and Interactive Systems

  • Architectural Support for Data Mining

    Marcel Holsheimer, Martin L. Kersten

    217

    PDF
  • From Facts to Rules to Decisions: An Overview of the FRD-1 System

    Ibrahim F. Imam, Ryszard S. Michalski

    229

    PDF
  • PolyAnalyst-A Machine Discovery System Inferring Functional Programs

    Mikhail V. Kiselev

    237

    PDF
  • Exploration of Simulation Experiments by Discovery

    Willi Klosgen

    251

    PDF
  • Applications of a Logical Discovery Engine

    Wim VanLaer, Luc Dehaspe, Luc DeRaedt

    263

    PDF
  • DICE: a Discovery Environment Integrating Inductive Bias

    J. Zucker, V. Corruble, J. Thomas, and G. Ramalho

    275

    PDF

Database-Specific Techniques

  • Database Mining in the Architecture of a Semantic Preprocessor for State Aware Query Optimization

    Sarabjot S. Anand, David A. Bell, and John G. Hughes

    287

    PDF
  • Extracting Domain Semantics for Knowledge Discovery in Relational Databases

    Roger H. L. Chiang, Terence M. Barron, and Veda C. Storey

    299

    PDF
  • Rule Induction for Semantic Query Optimization

    Chun-Nan Hsu, Craig A. Knoblock

    311

    PDF
  • Learning Data Trend Regularities From Databases in a Dynamic Environment

    Xiaohua Hu, Nick Cercone, and Jinshi Xie

    323

    PDF
  • Using Metagueries to Integrate Inductive Learning and Deductive Database Technology

    W. Shen, B. Mitbander, and KayLiang Ong, and C. Zaniolo

    335

    PDF

Methodology and Application Issues

  • Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning

    John M. Aronis, Foster J. Provost

    347

    PDF
  • Using Dynamic Time Warping to Find Patterns in Time Series

    Donald J. Berndt, James Clifford

    359

    PDF
  • A Comparison of Pruning Methods for Relational Concept Learning

    Johannes Furnkranz

    371

    PDF
  • A Case-Based Approach to Knowledge Navigation

    Kristian J. Hammond, Robin Burke, and Kathryn Schmitt

    383

    PDF
  • Geometric Comparison of Clarifications and Rule Sets

    T. J. Monk, R. S. Mitchell, L.A. Smith, and G. Holmes

    395

    PDF

Applications

  • Predicting Equity Returns from Securities Data with Minimal Rule Generation

    Chidanand Apte, Se June Hon

    407

    PDF
  • Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges

    Marek J. Druzdze, Clark Glymour

    419

    PDF
  • Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools

    Kenneth A. Kaufman

    431

    PDF
  • An Application of KEFM to the Analysis of Healthcare Information

    Christopher J. Matheus, Gregory Piatetsky-Shapiro, and Dwight McNeill

    441

    PDF
  • Proactive Network Maintenance Using Machine Learning

    R. Sasisekharan, V. Seshadri, and S. M. Weiss

    453

    PDF

Machine Discovery Terminology

  • Machine Discovery Terminology

    Willi Klosge, Jan M. Zytkow

    463

    PDF

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