Pattern detection and discovery : ESF Exploratory Workshop, London, UK, September 16 19, 2002 : proceedings
The collation of large electronic databases of scienti?c and commercial infor- tion has led to a dramatic growth of interest in methods for discovering struc- res in such databases. These methods often go under the general name of data mining. One important subdiscipline within data mining is concer...
Enregistré dans:
| Autres auteurs: | , , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Collection: | Lecture notes in computer science. Lecture notes in artificial intelligence
2447 |
| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Actes d'un séminaire tenu à Londres du 16 au 19 septembre 2002, d'après l écran-titre Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Pattern Detection and Discovery, ESF Exploratory Workshop, London, UK, September 16-19, 2002, Proceedings, David J. Hand, Niall M. Adams, Richard J. Bolton (eds.), Berlin, Springer, 2002, 1 vol. (XII-226 p.), Lecture notes in computer science, 3-540-44148-4 • Pattern Detection and Discovery, Texte imprimé, 9783662199459 |
Table des matières:
- General Issues
- Pattern Detection and Discovery
- Detecting Interesting Instances
- Complex Data: Mining Using Patterns
- Determining Hit Rate in Pattern Search
- An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes
- If You Can t See the Pattern, Is It There?
- Association Rules
- Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining
- Concise Representations of Association Rules
- Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
- Relational Association Rules: Getting Warmer
- Text and Web Mining
- Mining Text Data: Special Features and Patterns
- Modelling and Incorporating Background Knowledge in theWeb Mining Process
- Modeling Information in Textual Data Combining Labeled and Unlabeled Data
- Discovery of Frequent Word Sequences in Text
- Applications
- Pattern Detection and Discovery: The Case of Music Data Mining
- Discovery of Core Episodes from Sequences
- Patterns of Dependencies in Dynamic Multivariate Data.