The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.
Since the previous edition’s publication, great advances have been made in the field of data mining. Not only does thisThird Editionof Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology; mining stream; mining social networks; and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today’s most powerful data mining techniques.
|Series:||Morgan Kaufmann Series in Data Management Systems Series|
|Product dimensions:||7.80(w) x 9.30(h) x 1.70(d)|
About the Author
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.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.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.
Table of Contents
2. Data Preprocessing
3. Data Warehouse and OLAP Technology: An Overview
4. Data Cube Computation and Data Generalization
5. Mining Frequent Patterns, Associations, and Correlations
6. Classification and Prediction
7. Cluster Analysis
8. Mining Stream, Time-Series, and Sequence Data
9 Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
10. Mining Object, Spatial, Multimedia, Text, and Web Data
11. Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining
Most Helpful Customer Reviews
Are you a computer science student, application developer, and business professional; as well as, a researcher? If you are, then this book is for you! Authors Jiawei Han, Micheline Kamber, and Jian Pei, have done an outstanding job of writing a third edition of a book which explores the concepts and techniques of knowledge discovery and data mining. Han, Kamber and Pei, begin by providing an introduction to the multidisciplinary field of data mining. In addition, the authors introduce the general data features. The authors then focus on the techniques for data processing. Then, they look at the basic concepts, modeling, design architectures, and general implementations of data warehouses and OLAP; as well as, the relationship between data warehousing and other data generalization methods. Next, the authors take an in-depth look at cube technology, presenting a detailed study of methods of data cube computation, including Star-Cubing and high-dimensional OLAP methods. They continue with an in-depth look at the fundamental concepts, such as market basket analysis, with many techniques for frequent itemset mining presented in an organized way. In addition, the authors discuss methods for pattern mining in multilevel and multidimensional space, mining rare and negative patterns, mining colossal patterns and high-dimensional data, constraint-based pattern mining, and mining compressed or approximate patterns. The authors then introduce the basic concepts and methods for classification, including decision tree induction, Bayes classification, and rule-based classification. Then, they discuss advanced methods for classification, including Bayesian belief networks, the neural network technique of backpropagation , support vector machines, classification using frequent patterns, k-nearest-neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Next, the authors introduce the basic concepts and methods for data clustering, including an overview of basic cluster analysis methods, partitioning methods, hierarchical methods, density-based methods, and grid-based methods. They continue with a discussion of advanced methods for clustering, including probabilistic model-based clustering, clustering high-dimensional data, clustering graph and network data, and clustering with constraints. In addition, the authors introduce the basic concepts of outliers and outlier analysis, and discuss various outlier detection methods from the view of degree of supervision; as well as, from the view of approaches. Finally, the authors discuss trends, applications, and research frontiers in data mining. This most excellent book is not intended as an introduction to statistics, machine learning, database systems, or other such areas. Rather, the book is a comprehensive introduction to data mining.