Fabrice Guillet, Bruno Pinaud, Gilles Venturini and Djamel Abdelkader Zighed (eds),
« Advances In Knowledge Discovery and Management, Volume 4 »,
Series: Studies in Computational Intelligence,
Vol. 527, 2014, Springer.
ISBN: 978-3-642-25837-4, DOI: 10.1007/978-3-319-02999-3.
About this book
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’2012 Conference held in Bordeaux, France, on January 2012. This conference was the 12th edition 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 societe (EGC in French stands for “Extraction et Gestion des Connaissances” and means “Knowledge Discovery and Management”, or KDM).
Structure of the Book
This book is intended to be read by 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 “Knowledge Discovery and Data Mining” and “Classification and Feature Extraction or Selection”. The first part (6 chapters) deals with data clustering and data mining. The three remaining chapters of the second part are related to classification and feature extraction or feature selection.
Knowledge Discovery, Knowledge Management, Data Mining, Knowledge Engineering, Applications
Table des matières
Part I — Knowledge Discovery and Data Mining
- François Queyroi:
Optimizing a Hierarchical Community Structure of a Complex Network. 3-14
- Marc Boullé, Romain Guigourès, Fabrice Rossi:
Nonparametric Hierarchical Clustering of Functional Data. 15-35
- Francisco de A.T. de Carvalho, Yves Lechevallier, Thierry Despeyroux, Filipe M. de Melo:
Multi-view Clustering on Relational Data. 37-51
- Asma Ben Zakour, Sofian Maabout, Mohamed Mosbah, Marc Sistiaga:
Relaxing Time Granularity for Mining Frequent Sequences. 53-76
- Rim Faiz, Maha Amami, Aymen Elkhlifi:
Semantic Event Extraction from Biological Texts Using a Kernel-Based Method. 77-94
- Dhafer Lahbib, Marc Boullé, Dominique Laurent:
Supervised Pre-processing of Numerical Variables for Multi-Relational Data Mining. 95-109
Part II — Classification and Feature Extraction or Selection
- Hassan Chouaib, Florence Cloppet, Nicole Vincent:
Combination of Single Feature Classifiers for Fast Feature Selection. 113-131
- Laurent Vézard, Pierrick Legrand, Marie Chavent, Frédérique Faïta-Aïnseba:
Classification of EEG Signals by an Evolutionary Algorithm. 133-153
- Thanh-Nghi Doan, François Poulet:
Large Scale Image Classification: Fast Feature Extraction, Multi-codebook Approach and Multi-core SVM Training. 155-172