Pattern Detection and Discovery : ESF Exploratory Workshop, London, UK, September 16-19, 2002. / edited by David J Hand, Niall, M. Adams, Richard J. Bolton.

Format:

Book

Contributor:

Hand, D. J. (David J.), 1950- editor.

Adams, Niall M., 1968- editor.

Bolton, Richard J., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2447.

Lecture Notes in Artificial Intelligence ; 2447

Language:

English

Subjects (All):

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.

Local Subjects:

Database Management.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Data Structures and Information Theory.

Probability and Statistics in Computer Science.

Information Storage and Retrieval.

Physical Description:

1 online resource (XII, 232 pages).

Edition:

First edition 2002.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.

System Details:

text file PDF

Summary:

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 concerned with the identi?cation and detection of anomalous, interesting, unusual, or valuable - cords or groups of records, which we call patterns. Familiar examples are the detection of fraud in credit-card transactions, of particular coincident purchases in supermarket transactions, of important nucleotide sequences in gene sequence analysis, and of characteristic traces in EEG records. Tools for the detection of such patterns have been developed within the data mining community, but also within other research communities, typically without an awareness that the - sic problem was common to many disciplines. This is not unreasonable: each of these disciplines has a large literature of its own, and a literature which is growing rapidly. Keeping up with any one of these is di?cult enough, let alone keeping up with others as well, which may in any case be couched in an - familiar technical language. But, of course, this means that opportunities are being lost, discoveries relating to the common problem made in one area are not transferred to the other area, and breakthroughs and problem solutions are being rediscovered, or not discovered for a long time, meaning that e?ort is being wasted and opportunities may be lost.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-45728-2

9783540457282

Access Restriction:

Restricted for use by site license.