Advances in Knowledge Discovery and Management Volume 7 / [electronic resource] :
edited by Bruno Pinaud, Fabrice Guillet, Bruno Cremilleux, Cyril de Runz.
- 1st ed. 2018.
- XIII, 147 p. 46 illus., 35 illus. in color. online resource.
- Studies in Computational Intelligence, 732 1860-9503 ; .
- Studies in Computational Intelligence, 732 .
A Combined Approach for Ontology Enrichment from Textual and Open Data -- C-SPARQL Extension for Sampling RDF Graphs Streams -- Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.
This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of “Knowledge Discovery and Management” (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC’2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.
9783319654065
10.1007/978-3-319-65406-5 doi
Computational intelligence.
Artificial intelligence.
Data mining.
Knowledge management.
Computational Intelligence.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Knowledge Management.
Q342
006.3
A Combined Approach for Ontology Enrichment from Textual and Open Data -- C-SPARQL Extension for Sampling RDF Graphs Streams -- Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.
This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of “Knowledge Discovery and Management” (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC’2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.
9783319654065
10.1007/978-3-319-65406-5 doi
Computational intelligence.
Artificial intelligence.
Data mining.
Knowledge management.
Computational Intelligence.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Knowledge Management.
Q342
006.3