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Inductive Databases and Constraint-Based Data Mining

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Inductive Databases and Constraint-Based Data Mining Cover

 

Synopses & Reviews

Publisher Comments:

This book presents inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The book provides an overview of the state-of-the art in this novel research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the unification of pattern mining approaches through constraint programming, the clarification of the relationship between mining local patterns and global models, and the proposed integrative frameworks and approaches for inductive databases. On the application side, applications to practically relevant problems from bioinformatics are presented to attract additional attention from a wider audience. The primary audience consists of scientists and graduate students in computer science and bio-informatics. Potential readers are likely to attend conferences on databases, data mining/ machine learning, and bio-informatics.

Synopsis:

This presentation of emergent research topics at the intersection of data mining and database research covers the state of the art in this field as well as examining its applications to problems of practical relevance such as those in bioinformatics.

Table of Contents

Part 1 Introduction & Framework.- Twelve Years After: A Historical Perspective on Inductive Databases.- A Data Mining Framework and Ontology.- Data Mining Query Languages, Mining Views, and Algebras.- The Assessment of Data Mining Results through Randomization.- Part 2 Constraint-based Mining Techniques.- Generalizing Item set Mining in a Constraint Programming Setting From Local Patterns to Classification Models.- Constrained Induction of Predictive Clustering Trees.- Constrained Clustering: An Overview.- Probabilistic Inductive Querying Using ProbLog.- Part 3 Inductive Databases: Integration Approaches.- An Inductive Database based on Mining.- SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model Experiment Databases.- Inductive Scientific Databases for Equation Discovery.- Part 4 Applications in Bioinformatics.- Robot Scientists, Inductive Queries, and Drug Design.- Predicting Gene Function using Predictive Clustering Trees.- Analysis of Gene Expression Data with Predictive Clustering Trees Using a Solver Over String Pattern Domain to Analyse Gene Promoter Sequences.

Product Details

ISBN:
9781441977373
Author:
Dzeroski, Saso (edt)
Publisher:
Springer
Editor:
Dzeroski, Saso
Editor:
Goethals, Bart
Editor:
Panov, Pan?e
Author:
Panov, Panče
Author:
Deroski, Sao
Author:
Goethals, Bart
Author:
Dzeroski, Saso
Author:
Panov, Pan?e
Subject:
Database Management - Database Mining
Subject:
Intelligence (AI) & Semantics
Subject:
Bioinformatics
Subject:
Database Management - General
Subject:
Data mining
Subject:
Inductive databases
Subject:
pattern mining
Subject:
Predictive models
Subject:
structured data
Subject:
Database management
Subject:
Data Mining and Knowledge Discovery
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Computational Biology/Bioinformatics
Subject:
Computers-Reference - General
Subject:
Artificial Intelligen
Subject:
ce (incl. Robotics)
Copyright:
Edition Description:
2010
Publication Date:
20101102
Binding:
HARDCOVER
Language:
English
Pages:
476
Dimensions:
235 x 155 mm 1850 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » Bioinformatics
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Software Engineering » Software Management
Science and Mathematics » Chemistry » Biochemistry

Inductive Databases and Constraint-Based Data Mining New Hardcover
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Product details 476 pages Springer - English 9781441977373 Reviews:
"Synopsis" by , This presentation of emergent research topics at the intersection of data mining and database research covers the state of the art in this field as well as examining its applications to problems of practical relevance such as those in bioinformatics.
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