
Advances in Knowledge Discovery and Management
Volume 8
Springer (Publisher)
Published on 1. July 2019
Book
Hardback
XVIII, 183 pages
978-3-030-18128-4 (ISBN)
Description
This book highlights novel research in Knowledge Discovery and Management (KDM), gathering the extended, peer-reviewed versions of outstanding papers presented at the annual conferences EGC'2017 & EGC'2018. The EGC conference cycle was founded by the International French-speaking EGC society ("Extraction et Gestion des Connaissances") in 2003, and has since become a respected fixture among the French-speaking community. In addition to the annual conference, the society organizes various other events in order to promote exchanges between researchers and companies concerned with KDM and its applications to business, administration, industry and public organizations.
Addressing novel research in data science, semantic Web, clustering, and classification, the content presented here will chiefly benefit researchers interested in these fields, including Ph.D./M.Sc. students, at public and private laboratories alike.More details
Series
Edition
2019 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
42 s/w Abbildungen, 33 farbige Abbildungen
XVIII, 183 p. 75 illus., 33 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 17 mm
Weight
477 gr
ISBN-13
978-3-030-18128-4 (9783030181284)
DOI
10.1007/978-3-030-18129-1
Schweitzer Classification
Other editions
Additional editions

Bruno Pinaud | Fabrice Guillet | Fabien Gandon
Advances in Knowledge Discovery and Management
Volume 8
Book
08/2020
Springer
€106.99
Shipment within 7-9 days

Bruno Pinaud | Fabrice Guillet | Fabien Gandon
Advances in Knowledge Discovery and Management
Volume 8
E-Book
06/2019
Springer
€96.29
Available for download
Content
Part I: Clustering.- Chapter 1. Model based co-clustering of mixed numerical and binary data.- Chapter 2. Co-clustering based exploratory analysis of mixed-type data tables.- Part II: Textual Data.- Chapter 3. Automatically selecting complementary vector representations for semantic textual similarity, etc.