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Developing Multi-Database M...
Multi-database mining is recognized as an important and strategic area of research in data mining. The authors discuss the essential issues relating to the systematic and efficient development of multi-database mining applications, and present approaches to the development of data warehouses at different branches, demonstrating how carefully selected multi-database mining techniques contribute to successful real-world applications. In showing and quantifying how the efficiency of a multi-database mining application can be improved by processing more patterns, the book also covers other essential design aspects. These are carefully investigated and include a determination of an appropriate multi-database mining model, how to select relevant databases, choosing an appropriate pattern synthesizing technique, representing pattern space, and constructing an efficient algorithm. The authors illustrate each of these development issues either in the context of a specific problem at hand, or via some general settings. Developing Multi-Database Mining Applications will be welcomed by practitioners, researchers and students working in the area of data mining and knowledge discovery
Monografía
monografia Rebiun06427410 https://catalogo.rebiun.org/rebiun/record/Rebiun06427410 cr nn 008mamaa 100623s2010 xxk| s |||| 0|eng d 9781849960441 978-1-84996-044-1 10.1007/978-1-84996-044-1 doi UPNA0406531 UMO 87018 UR0323815 UNF bicssc UYQE bicssc COM021030 bisacsh 006.312 23 Adhikari, Animesh Developing Multi-Database Mining Applications Recurso electrónico-En línea] by Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz London Springer London Imprint: Springer 2010 London London Springer London Imprint: Springer X, 130p. digital X, 130p. Advanced Information and Knowledge Processing 1610-3947 Computer Science (Springer-11645) Introduction -- An Extended Model of Local Pattern Analysis -- Mining Multiple Large Databases -- Mining Patterns of Select Items in Multiple Databases -- Enhancing Quality of Knowledge Synthesized from Multi-database Mining -- Efficient Clustering of Databases Induced by Local Patterns -- A Framework for Developing Effective Multi-database Mining Applications Accesible sólo para usuarios de la UPV Recurso a texto completo Multi-database mining is recognized as an important and strategic area of research in data mining. The authors discuss the essential issues relating to the systematic and efficient development of multi-database mining applications, and present approaches to the development of data warehouses at different branches, demonstrating how carefully selected multi-database mining techniques contribute to successful real-world applications. In showing and quantifying how the efficiency of a multi-database mining application can be improved by processing more patterns, the book also covers other essential design aspects. These are carefully investigated and include a determination of an appropriate multi-database mining model, how to select relevant databases, choosing an appropriate pattern synthesizing technique, representing pattern space, and constructing an efficient algorithm. The authors illustrate each of these development issues either in the context of a specific problem at hand, or via some general settings. Developing Multi-Database Mining Applications will be welcomed by practitioners, researchers and students working in the area of data mining and knowledge discovery Reproducción electrónica Forma de acceso: Web Computer science Data mining Computer Science Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Ramachandrarao, Pralhad Pedrycz, Witold SpringerLink (Servicio en línea) Springer eBooks Springer eBooks Printed edition 9781849960434 Advanced Information and Knowledge Processing 1610-3947