Pattern Detection and Discovery ESF Exploratory Workshop, London, UK, September 16-19, 2002. /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Hand, David J. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Adams, Niall, M. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Bolton, Richard J. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.
• Έκδοση: 1st ed. 2002.
• Σειρά: Lecture Notes in Artificial Intelligence ; 2447
• Θέματα: Database management.; Artificial intelligence.; Algorithms.; Data structures (Computer science).; Mathematical statistics.; Information storage and retrieval.; Database Management.; Artificial Intelligence.; Algorithm Analysis and Problem Complexity.; Data Structures and Information Theory.; Probability and Statistics in Computer Science.; Information Storage and Retrieval.

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...

Πίνακας περιεχομένων:

• 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.