Jiawei Han, Jian Pei, Hanghang Tong

Data Mining (eBook, ePUB)

Concepts and Techniques

• Format: ePub

Produktbeschreibung

Data Mining: Concepts and Techniques, Fourth Edition provides the theories and methods for processing data or information used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from collected data, known as KDD. The book focuses on the feasibility, usefulness, effectiveness and scalability of techniques of large datasets. After describing data mining, the authors explain the methods of knowing, preprocessing, processing and warehousing data. They then present 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. Users from computer science students, application developers, business professionals, and researchers who seek information on data mining will find this resource very helpful.

• 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 needed to get the most out of your data

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 752
• Erscheinungstermin: 2. Juli 2022
• Englisch
• ISBN-13: 9780128117613
• Artikelnr.: 64270526

Autorenporträt

Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. 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. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his "contributions to the foundation, methodology and applications of data mining and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his "contributions to data mining and knowledge discovery. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences.Hanghang Tong Ph.D. is currently an associate professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Before that he was an associate professor at the School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. He received his M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2008 and 2009, both in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015), four best paper awards (TUP'14, CIKM'12, SDM'08, ICDM'06), seven 'bests of conference', 1 best demo, honorable mention (SIGMOD'17), and 1 best demo candidate, second place (CIKM'17). He has published over 100 refereed articles. He is the Editor-in-Chief of SIGKDD Explorations (ACM), an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Knowledge and Information Systems (Springer) and Neurocomputing Journal (Elsevier); and has served as a program committee member in multiple data mining, database and artificial intelligence venues (e.g., SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc.).

Inhaltsangabe

1. Introduction
2. Data, measurements, and data processing
3. Data warehousing and online analytical processing
4. Pattern mining: basic concepts and methods
5. Pattern mining: advanced methods
6. Classification: basic concepts and methods
7. Classification: advanced methods
8. Cluster analysis: basic concepts and methods
9. Cluster analysis: advanced methods
10. Deep learning
11. Outlier Detection
12. Data mining trends and research frontiers
Appendix: Mathematical background