Advances in Knowledge Discovery and Management Volume 6 / [electronic resource] :
edited by Fabrice Guillet, Bruno Pinaud, Gilles Venturini.
- 1st ed. 2017.
- XXI, 278 p. 81 illus., 61 illus. in color. online resource.
- Studies in Computational Intelligence, 665 1860-9503 ; .
- Studies in Computational Intelligence, 665 .
Part I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization -- On making skyline queries resistant to outliers -- Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures -- Exact and Approximate Minimal Pattern Mining -- Part II: Comparison of proximity measures for a topological discrimination -- Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit -- A novel approach to feature selection based on quality estimation metrics -- Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining -- Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation -- Multi-layer ontologies for integrated 3D shape segmentation and annotation -- Ontology Alignment Using Web Linked Ontologies as Background Knowledge -- LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks -- Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks.
This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.
9783319457635
10.1007/978-3-319-45763-5 doi
Data mining.
Computational intelligence.
Artificial intelligence.
Data Mining and Knowledge Discovery.
Computational Intelligence.
Artificial Intelligence.
QA76.9.D343
006.312
Part I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization -- On making skyline queries resistant to outliers -- Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures -- Exact and Approximate Minimal Pattern Mining -- Part II: Comparison of proximity measures for a topological discrimination -- Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit -- A novel approach to feature selection based on quality estimation metrics -- Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining -- Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation -- Multi-layer ontologies for integrated 3D shape segmentation and annotation -- Ontology Alignment Using Web Linked Ontologies as Background Knowledge -- LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks -- Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks.
This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.
9783319457635
10.1007/978-3-319-45763-5 doi
Data mining.
Computational intelligence.
Artificial intelligence.
Data Mining and Knowledge Discovery.
Computational Intelligence.
Artificial Intelligence.
QA76.9.D343
006.312