Principles of Data Mining and Knowledge Discovery : Third European Conference, PKDD'99 Prague, Czech Republic, September 15-18, 1999 Proceedings / edited by Jan Zytkow, Jan Rauch.

Format:

Book

Contributor:

Żytkow, Jan M., editor.

Rauch, Jan, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1704.

Lecture Notes in Artificial Intelligence ; 1704

Language:

English

Subjects (All):

Database management.

Artificial intelligence.

Information storage and retrieval.

Multimedia systems.

Mathematical statistics.

Information technology.

Business--Data processing.

Business.

Database Management.

Artificial Intelligence.

Information Storage and Retrieval.

Multimedia Information Systems.

Probability and Statistics in Computer Science.

IT in Business.

Local Subjects:

Database Management.

Artificial Intelligence.

Information Storage and Retrieval.

Multimedia Information Systems.

Probability and Statistics in Computer Science.

IT in Business.

Physical Description:

1 online resource (XIV, 593 pages).

Edition:

First edition 1999.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1999.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

Contents:

Session 1A - Time Series

Scaling up Dynamic Time Warping to Massive Datasets

The Haar Wavelet Transform in the Time Series Similarity Paradigm

Rule Discovery in Large Time-Series Medical Databases

Session 1B - Applications

Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE

Applying Data Mining Techniques to Wafer Manufacturing

An Application of Data Mining to the Problem of the University Students' Dropout Using Markov Chains

Session 2A - Taxonomies and Partitions

Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD

Taxonomy Formation by Approximate Equivalence Relations, Revisited

On the Use of Self-Organizing Maps for Clustering and Visualization

Speeding Up the Search for Optimal Partitions

Session 2B - Logic Methods

Experiments in Meta-level Learning with ILP

Boolean Reasoning Scheme with Some Applications in Data Mining

On the Correspondence between Classes of Implicational and Equivalence Quantifiers

Querying Inductive Databases via Logic-Based User-Defined Aggregates

Session 3A - Distributed and Multirelational Databases

Peculiarity Oriented Multi-database Mining

Knowledge Discovery in Medical Multi-databases: A Rough Set Approach

Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates

Session 3B - Text Mining and Feature Selection

Text Mining via Information Extraction

TopCat: Data Mining for Topic Identification in a Text Corpus

Selection and Statistical Validation of Features and Prototypes

Session 4A - Rules and Induction

Taming Large Rule Models in Rough Set Approaches

Optimizing Disjunctive Association Rules

Contribution of Boosting in Wrapper Models

Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme

Session 5A - Interesting and Unusual

Heuristic Measures of Interestingness

Enhancing Rule Interestingness for Neuro-fuzzy Systems

Unsupervised Profiling for Identifying Superimposed Fraud

OPTICS-OF: Identifying Local Outliers

Posters

Selective Propositionalization for Relational Learning

Circle Graphs: New Visualization Tools for Text-Mining

On the Consistency of Information Filters for Lazy Learning Algorithms

Using Genetic Algorithms to Evolve a Rule Hierarchy

Mining Temporal Features in Association Rules

The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning

Analyzing an Email Collection Using Formal Concept Analysis

Business Focused Evaluation Methods: A Case Study

Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions

Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation?

Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts

A Fuzzy Beam-Search Rule Induction Algorithm

An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining

Extension to C-means Algorithm for the Use of Similarity Functions

Predicting Chemical Carcinogenesis Using Structural Information Only

LA - A Clustering Algorithm with an Automated Selection of Attributes, Which is Invariant to Functional Transformations of Coordinates

Association Rule Selection in a Data Mining Environment

Multi-relational Decision Tree Induction

Learning of Simple Conceptual Graphs from Positive and Negative Examples

An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction

ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables

Efficient Mining of High Confidence Association Rules without Support Thresholds

A Logical Approach to Fuzzy Data Analysis

AST: Support for Algorithm Selection with a CBR Approach

Efficient Shared Near Neighbours Clustering of Large Metric Data Sets

Discovery of "Interesting" Data Dependencies from a Workload of SQL Statements

Learning from Highly Structured Data by Decomposition

Combinatorial Approach for Data Binarization

Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method

Automated Discovery of Polynomials by Inductive Genetic Programming

Diagnosing Acute Appendicitis with Very Simple Classification Rules

Rule Induction in Cascade Model Based on Sum of Squares Decomposition

Maintenance of Discovered Knowledge

A Divisive Initialisation Method for Clustering Algorithms

A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series

Mining Lemma Disambiguation Rules from Czech Corpora

Adding Temporal Semantics to Association Rules

Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept

Discovering Rules in Information Trees

Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections

Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking

Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions

Towards Discovery of Information Granules

Classification Algorithms Based on Linear Combinations of Features

Managing Interesting Rules in Sequence Mining

Support Vector Machines for Knowledge Discovery

Regression by Feature Projections

Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms

Tutorials

Data Mining for Robust Business Intelligence Solutions

Query Languages for Knowledge Discovery in Databases

The ESPRIT Project CreditMine and its Relevance for the Internet Market

Logics and Statistics for Association Rules and Beyond

Data Mining for the Web

Relational Learning and Inductive Logic Programming Made Easy.

Other Format:

Printed edition:

ISBN:

978-3-540-48247-5

9783540482475

Access Restriction:

Restricted for use by site license.