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.
