Data Warehouse and Data Mining Tutorial

Author: Chen Wenwei / Country: Mainland China
Publisher:
Publish Date: 2006-08-01
Features: Data warehousing and data mining are both methods of extracting information and knowledge from data resources to assist in decision-making. Due to the abundance of data resources, the effectiveness of data warehousing and data mining in decision support is highly significant. This book systematically introduces the principles of data warehousing, online analytical processing (OLAP), data warehouse design and development, decision support applications of data warehouses, the principles of data mining, information-theoretic decision tree methods, rough set methods based on set theory, association rules, formula discovery, neural networks, genetic algorithms, text mining, and web mining, as well as the development of data warehousing and data mining.
The systematic introduction to data warehousing in this book emphasizes the essence of decision support. It introduces the theoretical foundations and implementation methods of various data mining techniques, illustrated with examples. The book's distinctive feature lies in explaining the essence of data warehousing and data mining through their emergence and evolution, and illustrating their principles with practical examples, making it easier for readers to learn and master. It is suitable for undergraduate and graduate students.
Data warehousing and data mining are both methods of extracting information and knowledge from data resources to assist in decision-making. Due to the abundance of data resources, the effectiveness of data warehousing and data mining in decision support is highly significant. This book systematically introduces the principles of data warehousing, online analytical processing (OLAP), data warehouse design and development, decision support applications of data warehouses, the principles of data mining, information-theoretic decision tree methods, rough set methods based on set theory, association rules, formula discovery, neural networks, genetic algorithms, text mining, and web mining, as well as the development of data warehousing and data mining.
The systematic introduction to data warehousing in this book emphasizes the essence of decision support. It introduces the theoretical foundations and implementation methods of various data mining techniques, illustrated with examples. The book's distinctive feature lies in explaining the essence of data warehousing and data mining through their emergence and evolution, and illustrating their principles with practical examples, making it easier for readers to learn and master. It is suitable for undergraduate and graduate students.