HAN AND KAMBER DATA MINING EBOOK PDF

Mezirn Advanced Backend Code Optimization. Fundamental Approaches to Software Engineering. An Introduction to Description Logic. A General Introduction to Data Analytics. Measurement, Modelling and Evaluation of Computing Systems. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data.

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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.

This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data Read more Collapse About the author Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign.

He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems.

His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. Read more.

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