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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. Key features: Covers both core methods and cutting-edge research Algorithmic approach with open-source implementations Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference Supplementary website with lecture slides, videos, project ideas, and more.
Data Mining and Analysis
Data Analysis Foundations
Numeric Attributes
Categorical Attributes
Graph Data
Kernel Methods
High-Dimensional Data
Dimensionality Reduction
Frequent Pattern Mining
Itemset Mining
Summarizing Itemsets
Sequence Mining
Graph Pattern Mining
Pattern and Rule Assessment
Clustering
Representative-based Clustering
Hierarchical Clustering
Density-based Clustering
Spectral and Graph Clustering
Clustering Validation
Classification
Probabilistic Classification
Decision Tree Classifier
Linear Discriminant Analysis
Support Vector Machines
Classification Assessment