Pattern Detection and Discovery

ESF Exploratory Workshop, London, UK, September 16-19, 2002.

AvDavid J Hand,M. Adams, Niall

Häftad, Engelska, 2002

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of an international workshop on Pattern Detection and Discovery organized by the European Science Foundation in London, UK in September 2002. The 17 revised full papers presented were carefully selected and reviewed for inclusion in this state-of-the-art book. Six papers present an introduction and general issues in the emerging field. Four papers are devoted to association rules. Four papers deal with various aspects of text mining and Web mining, and three papers explore advanced applications.

Produktinformation

Utgivningsdatum:2002-09-04
Mått:155 x 235 x 14 mm
Vikt:376 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:232
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540441489

Utforska kategorier

Innehållsförteckning

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.