Principles of Data Mining and Knowledge Discovery

First European Symposium, PKDD '97, Trondheim, Norway, June 24-27, 1997 Proceedings
1997
ISBN: 978-3-540-63223-8
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 1263, 404 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1280 g

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Zytkow, Jan

Komorowski, Jan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware SAP
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management

Inhaltsverzeichnis

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 reliabilityof 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.