Developing Multi-Database Mining Applications [electronic resource] / by Animesh Adhikari, Pralhad Ramachandrarao, Witold Pedrycz.

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.

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.

ZDB-2-SCS