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This book is about inductive databases and constraint-based data
mining, emerging research topics lying at the intersection of data
mining and database research. The aim of the book as to provide an
overview of the state-of- the art in this novel and - citing
research area. Of special interest are the recent methods for
constraint-based mining of global models for prediction and
clustering, the uni?cation of pattern mining approaches through
constraint programming, the clari?cation of the re- tionship
between mining local patterns and global models, and the proposed
in- grative frameworks and approaches for inducive databases. On
the application side, applications to practically relevant problems
from bioinformatics are presented. Inductive databases (IDBs)
represent a database view on data mining and kno- edge discovery.
IDBs contain not only data, but also generalizations (patterns and
models) valid in the data. In an IDB, ordinary queries can be used
to access and - nipulate data, while inductive queries can be used
to generate (mine), manipulate, and apply patterns and models. In
the IDB framework, patterns and models become "?rst-class citizens"
and KDD becomes an extended querying process in which both the data
and the patterns/models that hold in the data are queried.
This book is about inductive databases and constraint-based data
mining, emerging research topics lying at the intersection of data
mining and database research. The aim of the book as to provide an
overview of the state-of- the art in this novel and - citing
research area. Of special interest are the recent methods for
constraint-based mining of global models for prediction and
clustering, the uni?cation of pattern mining approaches through
constraint programming, the clari?cation of the re- tionship
between mining local patterns and global models, and the proposed
in- grative frameworks and approaches for inducive databases. On
the application side, applications to practically relevant problems
from bioinformatics are presented. Inductive databases (IDBs)
represent a database view on data mining and kno- edge discovery.
IDBs contain not only data, but also generalizations (patterns and
models) valid in the data. In an IDB, ordinary queries can be used
to access and - nipulate data, while inductive queries can be used
to generate (mine), manipulate, and apply patterns and models. In
the IDB framework, patterns and models become "?rst-class citizens"
and KDD becomes an extended querying process in which both the data
and the patterns/models that hold in the data are queried.
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Artificial Intelligence and Machine Learning - 34th Joint Benelux Conference, BNAIC/Benelearn 2022, Mechelen, Belgium, November 7–9, 2022, Revised Selected Papers (1st ed. 2023)
Toon Calders, Celine Vens, Jefrey Lijffijt, Bart Goethals
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R1,782
Discovery Miles 17 820
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Ships in 10 - 15 working days
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This book contains a selection of the best papers of the 34th
Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN
2022, held in Mechelen, Belgium, in November 2022.The 11 papers
presented in this volume were carefully reviewed and selected from
134 regular submissions. They address various aspects of artificial
intelligence such as natural language processing, agent technology,
game theory, problem solving, machine learning, human-agent
interaction, AI and education, and data analysis.
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