Principles of Data Mining and Knowledge Discovery : First European Symposium, PKDD '97, Trondheim, Norway, June 24-27, 1997 Proceedings / edited by Jan Komorowski, Jan Zytkow.

Format:

Book

Contributor:

Komorowski, J. (Jan), editor.

Żytkow, Jan M., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1263.

Lecture Notes in Artificial Intelligence ; 1263

Language:

English

Subjects (All):

Artificial intelligence.

Information storage and retrieval.

Multimedia systems.

Mathematical statistics.

Information technology.

Business--Data processing.

Business.

Artificial Intelligence.

Information Storage and Retrieval.

Multimedia Information Systems.

Probability and Statistics in Computer Science.

IT in Business.

Local Subjects:

Artificial Intelligence.

Information Storage and Retrieval.

Multimedia Information Systems.

Probability and Statistics in Computer Science.

IT in Business.

Physical Description:

1 online resource (XII, 404 pages).

Edition:

First edition 1997.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '97, held in Trondheim, Norway, in June 1997. The volume presents a total of 38 revised full papers together with abstracts of one invited talk and four tutorials. Among the topics covered are data and knowledge representation, statistical and probabilistic methods, logic-based approaches, man-machine interaction aspects, AI contributions, high performance computing support, machine learning, automated scientific discovery, quality assessment, and applications.

Contents:

Knowledge discovery - A control theory perspective

Modelling customer retention with Rough Data Models

Share based measures for itemsets

Parallel knowledge discovery using domain generalization graphs

Rough set theory and rule induction techniques for discovery of attribute dependencies in medical information systems

Logical calculi for knowledge discovery in databases

Extraction of experts' decision process from clinical databases using rough set model

Discovering of health risks and case-based forecasting of epidemics in a health surveillance system

An algorithm for multi-relational discovery of subgroups

Finding similar time series

Exploration of document collections with self-organizing maps: A novel approach to similarity representation

Pattern based browsing in document collections

Induction of fuzzy characteristic rules

Regression-based classification methods and their comparison with decision tree algorithms

Attribute discovery and rough sets

Generation of rules from incomplete information systems

Knowledge discovery from software engineering data: Rough set analysis and its interaction with goal-oriented measurement

Efficient multisplitting on numerical data

SNOUT: An intelligent assistant for exploratory data analysis

Exploratory analysis of biochemical processes using hybrid modeling methods

Using signature files for querying time-series data

A new and versatile method for association generation

Bivariate decision trees

Towards process-oriented tool support for knowledge discovery in databases

A connectionist approach to structural similarity determination as a basis of clustering, classification and feature detection

Searching for relational patterns in data

Finding spatial clusters

Interactive interpretation of hierarchical clustering

The principle of transformation between efficiency and effectiveness: Towards a fair evaluation of the cost-effectiveness of KDD techniques

Recognizing reliability of discovered knowledge

Clustering techniques in biological sequence analysis

TOAS intelligence mining; analysis of natural language processing and computational linguistics

Algorithms for constructing of decision trees

Mining in the phrasal frontier

Mining time series using rough sets - A case study

Neural networks design: Rough set approach to continuous data

On meta levels of an organized society of KDD agents

Using neural network to extract knowledge from database

Induction of strong feature subsets

Rough sets for data mining and knowledge discovery

Techniques and applications of KDD

A tutorial introduction to high performance data mining

Data mining in the telecommunications industry.

Other Format:

Printed edition:

ISBN:

978-3-540-69236-2

9783540692362

Access Restriction:

Restricted for use by site license.