Knowledge Discovery in Multiple Databases

Shichao Zhang

ISBN 10: 1447110501 / ISBN 13: 9781447110507

Edité par Springer London, 2012

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

N° de réf. du vendeur 9781447110507

Détails bibliographiques

• Titre: Knowledge Discovery in Multiple Databases
• Éditeur: Springer London
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 1447110501
• ISBN à 13 chiffres: 9781447110507
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 14 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 14 millimètres de profondeur
• Poids: 382 grammes

A propos de ce titre

Synopsis

A state of the art research monograph that develops the specific and increasingly important area of multiple database mining.

Présentation de l'éditeur

The Web has emerged as a large, distributed data repository, and information on the Internet and in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. This challenge has attracted wide interest from both academia and the industry. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, and presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, and efficient algorithms for pattern discovery from multiple databases are described. Knowledge Discovery in Multiple Databases is suitable for researchers, professionals and students in data mining, distributed data analysis, and machine learning, who are interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might involve knowledge discovery in databases and data mining.