Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
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This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
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For a rapidly evolving field like data mining, it is difficult to compose “typical” exercises and even more difficult to work out “standard” answers. Some of the exercises in Data Mining: Concepts and Techniques are themselves good research topics that may lead to future Master or Ph.D. Therefore, our solution manual was prepared. 'Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab. Solution: Data warehousing and data mining! April 3, 2003 Data Mining: Concepts and Techniques 9 Data Mining Functionalities (3)! Outlier analysis! Outlier: a data object that does not comply with the general behavior.
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March 2023
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