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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.
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Discovery Science - 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings (Paperback, 2014 ed.)
Saso Dzeroski, Pance Panov, Dragi Kocev, Ljupco Todorovski
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R2,703
Discovery Miles 27 030
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 17th International
Conference on Discovery Science, DS 2014, held in Bled, Slovenia,
in October 2014. The 30 full papers included in this volume were
carefully reviewed and selected from 62 submissions. The papers
cover topics such as: computational scientific discovery; data
mining and knowledge discovery; machine learning and statistical
methods; computational creativity; mining scientific data; data and
knowledge visualization; knowledge discovery from scientific
literature; mining text, unstructured and multimedia data; mining
structured and relational data; mining temporal and spatial data;
mining data streams; network analysis; discovery informatics;
discovery and experimental workflows; knowledge capture and
scientific ontologies; data and knowledge integration; logic and
philosophy of scientific discovery; and applications of
computational methods in various scientific domains.
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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