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This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.

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This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

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to Data Mining Principles.- Data Warehousing, Data Mining, and OLAP.- Data Marts and Data Warehouse.- Evolution and Scaling of Data Mining Algorithms.- Emerging Trends and Applications of Data Mining.- Data Mining Trends and Knowledge Discovery.- Data Mining Tasks, Techniques, and Applications.- Data Mining: an Introduction - Case Study.- Data Mining & KDD.- Statistical Themes and Lessons for Data Mining.- Theoretical Frameworks for Data Mining.- Major and Privacy Issues in Data Mining and Knowledge Discovery.- Active Data Mining.- Decomposition in Data Mining - A Case Study.- Data Mining System Products and Research Prototypes.- Data Mining in Customer Value and Customer Relationship Management.- Data Mining in Business.- Data Mining in Sales Marketing and Finance.- Banking and Commercial Applications.- Data Mining for Insurance.- Data Mining in Biomedicine and Science.- Text and Web Mining.- Data Mining in Information Analysis and Delivery.- Data Mining in Telecommunications and Control.- Data Mining in Security.

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