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.
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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. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners.
The focus is data—all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique.
Summing Up: Highly recommended. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering.
The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book. It adds cited material from about , a new section on visualization, and pattern mining with the more recent cluster methods.
Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. Also, researchers and analysts from other disciplines--for example, epidemiologists, financial analysts, and psychometric researchers--may find the material very useful. Students should have some background in statistics, database systems, and machine learning and some experience programming.
Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers. Chapter-end exercises are included. A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification.
The final chapter describes the current state of data mining research and active research areas. Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science specializing in artificial intelligence from Concordia University, Canada. 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. We are always looking for ways to improve customer experience on Elsevier.
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View on ScienceDirect. Hardcover ISBN: Imprint: Morgan Kaufmann. Published Date: 22nd June Page Count: For regional delivery times, please check When will I receive my book? Sorry, this product is currently out of stock. Flexible - Read on multiple operating systems and devices.
Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle. Institutional Subscription. Online Companion Materials. Instructor Ancillary Support Materials. Free Shipping Free global shipping No minimum order. Introduction Publisher Summary 1. Data Preprocessing Publisher Summary 3. Data Cube Technology Publisher Summary 5.
Advanced Pattern Mining Publisher Summary 7. Classification: Basic Concepts Publisher Summary 8. Classification: Advanced Methods Publisher Summary 9. Advanced Cluster Analysis Publisher Summary Outlier Detection Publisher Summary 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.
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Review by Dr. All the concepts are explained with numerical. On Data Mining: Concepts and Techniques. Was this review helpful? University of Illinois, Urbana Champaign. Simon Fraser University, Burnaby, Canada. If you wish to place a tax exempt order please contact us.
Data Mining: Concepts and Techniques