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Seriation in Combinatorial and Statistical Data Analysis
This monograph offers an original broad and very diverse exploration of the seriation domain in data analysis, together with building a specific relation to clustering. Relative to a data table crossing a set of objects and a set of descriptive attributes, the search for orders which correspond respectively to these two sets is formalized mathematically and statistically. State-of-the-art methods are created and compared with classical methods and a thorough understanding of the mutual relationships between these methods is clearly expressed. The authors distinguish two families of methods: Geometric representation methods Algorithmic and Combinatorial methods Original and accurate methods are provided in the framework for both families. Their basis and comparison is made on both theoretical and experimental levels. The experimental analysis is very varied and very comprehensive. Seriation in Combinatorial and Statistical Data Analysis has a unique character in the literature falling within the fields of Data Analysis, Data Mining and Knowledge Discovery. It will be a valuable resource for students and researchers in the latter fields.
Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.
Quality Measures in Data Mining
This book presents recent advances in quality measures in data mining.
Encyclopedia of Library and Information Science
"The Encyclopedia of Library and Information Science provides an outstanding resource in 33 published volumes with 2 helpful indexes. This thorough reference set--written by 1300 eminent, international experts--offers librarians, information/computer scientists, bibliographers, documentalists, systems analysts, and students, convenient access to the techniques and tools of both library and information science. Impeccably researched, cross referenced, alphabetized by subject, and generously illustrated, the Encyclopedia of Library and Information Science integrates the essential theoretical and practical information accumulating in this rapidly growing field."
Advances in Knowledge Discovery and Management
The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC’2011 Conference held in Brest, France, on January 2011. EGC stands for "Extraction et Gestion des connaissances" in French, and means "Knowledge Discovery and Management" or KDM. KDM is concerned with the works in computer science at the interface between data and knowledge; such as Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and Semantic Web. 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. This book has been structured in two parts. The first part, entitled “Data Mining, classification and queries”, deals with rule and pattern mining, with topological approaches and with OLAP. The second part of the book, entitled “Ontology and Semantic”, is related to knowledge-based and user-centered approaches in KDM.
