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This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects.
Based upon the authors' previous successful book on data mining and knowledge discovery, this new volume has been extensively expanded, making it an effective instructional tool for advanced-level undergraduate and graduate courses. This book offers:
Researchers, practitioners and students are certain to consider this volume an indispensable resource in successfully accomplishing the goals of their data mining projects.
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Knowledge, hidden in voluminous data repositories routinely created and maintained by today s applications, can be extracted by data mining. The next step is to transform this discovered knowledge into the inference mechanisms or simply the behavior of agents and multi-agent systems. Agent Intelligence Through Data Mining addresses this issue, as well as the arguable challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. This book contains a methodology, tools and techniques, and several examples of agent-based applications developed with this approach. This volume focuses mainly on the use of data mining for smarter, more efficient agents.
Agent Intelligence Through Data Mining is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science."
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