Database Support for Data Mining Applications : Discovering Knowledge with Inductive Queries / edited by Rosa Meo, Pier L. Lanzi, Mika Klemettinen.

Format:

Book

Contributor:

Meo, Rosa, editor.

Lanzi, Pier L., editor.

Klemettinen, Mika, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science 0302-9743 ; 2682.

Lecture Notes in Computer Science, 0302-9743 ; 2682

Language:

English

Subjects (All):

Artificial intelligence.

Database management.

Information storage and retrieval.

Artificial Intelligence.

Database Management.

Information Storage and Retrieval.

Local Subjects:

Artificial Intelligence.

Database Management.

Information Storage and Retrieval.

Physical Description:

1 online resource (XII, 332 pages).

Edition:

First edition 2004.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.

System Details:

text file PDF

Summary:

Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.

Contents:

Database Languages and Query Execution

Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach

Query Languages Supporting Descriptive Rule Mining: A Comparative Study

Declarative Data Mining Using SQL3

Towards a Logic Query Language for Data Mining

A Data Mining Query Language for Knowledge Discovery in a Geographical Information System

Towards Query Evaluation in Inductive Databases Using Version Spaces

The GUHA Method, Data Preprocessing and Mining

Constraint Based Mining of First Order Sequences in SeqLog

Support for KDD-Process

Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS

Frequent Itemset Discovery with SQL Using Universal Quantification

Deducing Bounds on the Support of Itemsets

Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data

Condensed Representations for Sets of Mining Queries

One-Sided Instance-Based Boundary Sets

Domain Structures in Filtering Irrelevant Frequent Patterns

Integrity Constraints over Association Rules.

Other Format:

Printed edition:

ISBN:

978-3-540-44497-8

9783540444978

Access Restriction:

Restricted for use by site license.