Includes index.

1. Data mining and analysis -- Part 1. Data analysis foundations -- 2.Numeric attributes -- 3.Categorical attributes -- 4. Graph data -- 5. Kernel methods -- 6.High-dimensional data -- 7.Dimensionality reduction -- Part 2. Frequent pattern mining -- 8. Itemset mining -- 9.Summarizing item sets -- 10. Sequence mining -- 11. Graph pattern mining -- 12. Pattern and rule assessment -- Part 3. Clustering -- 13. Representative-based clustering -- 14. Hierarchical clustering -- 15.Density-based clustering -- 16. Spectral and graph clustering -- 17. Clustering validation -- Part 4. Classification -- 18. Probabilistic classification -- 19. Decision tree classifier -- 20. Linear discriminant analysis -- 21. Support vector machines -- 22. Classification assessment.

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.