Advances In Knowledge Discovery And Management: Volume 5 (studies In Computational Intelligence)
by Fabrice Guillet /
2015 / English / PDF
5.4 MB Download
This book is a collection of representative and novel works done
in Data Mining, Knowledge Discovery, Clustering and
Classification that were originally presented in French at the
EGC'2013 (Toulouse, France, January 2013) and EGC'2014
Conferences (Rennes, France, January 2014). These conferences
were respectively the 13th and 14th editions of this event, which
takes place each year and which is now successful and well-known
in the French-speaking community. This community was structured
in 2003 by the foundation of the French-speaking EGC society (EGC
in French stands for "Extraction et Gestion des Connaissances"
and means "Knowledge Discovery and Management", or KDM).
This book is a collection of representative and novel works done
in Data Mining, Knowledge Discovery, Clustering and
Classification that were originally presented in French at the
EGC'2013 (Toulouse, France, January 2013) and EGC'2014
Conferences (Rennes, France, January 2014). These conferences
were respectively the 13th and 14th editions of this event, which
takes place each year and which is now successful and well-known
in the French-speaking community. This community was structured
in 2003 by the foundation of the French-speaking EGC society (EGC
in French stands for "Extraction et Gestion des Connaissances"
and means "Knowledge Discovery and Management", or KDM).
This book is aiming at all researchers interested in these
fields, including PhD or MSc students, and researchers from
public or private laboratories. It concerns both theoretical and
practical aspects of KDM. The book is structured in two parts
called "Applications of KDM to real datasets" and "Foundations of
KDM".
This book is aiming at all researchers interested in these
fields, including PhD or MSc students, and researchers from
public or private laboratories. It concerns both theoretical and
practical aspects of KDM. The book is structured in two parts
called "Applications of KDM to real datasets" and "Foundations of
KDM".
