Principles of data mining and knowledge discovery : First European Symposium, PKDD '97, Trondheim, Norway, June 24 27, 1997 : proceedings
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 tuto...
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| مؤلف مشترك: | |
|---|---|
| مؤلفون آخرون: | , |
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| سلاسل: | Lecture notes in computer science. Lecture notes in artificial intelligence
1263 |
| الموضوعات: | |
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Principles of data mining and knowledge discovery, First European Symposium, PKDD '97, Trondheim, Norway, June 24-27, 1997, proceedings, Jan Komorowski, Jan Zytkow, eds, 1997, Berlin, Springer, 1 vol. (IX-396 p.), Lecture notes in computer science, 3-540-63223-9 • Principles of Data Mining and Knowledge Discovery, Texte imprimé, 9783662163917 |
جدول المحتويات:
- 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.