Data Mining and Analysis

The fundamental algorithms in data mining and analysis are the basis for business intelligence and analytics, as well as automated methods to analyze patterns and models for all kinds of data. Data Mining and Analysis: Fundamental Concepts and Algorithms, a textbook for senior undergraduate and graduate data mining courses provides a comprehensive overview from an algorithmic perspective, integrating concepts from machine learning and statistics, with plenty of examples and exercises.

Book Description

Topics included: Data Mining and Analysis • Numeric Attributes • Categorical Attributes • Graph Data • Kernel Methods • High-dimensional Data • Dimensionality Reduction • Itemset Mining • Summarizing Itemsets • Sequence Mining • Graph Pattern Mining • Pattern and Rule Assessment • Representative-based Clustering • Hierarchical Clustering • Density-based Clustering • Spectral and Graph Clustering • Clustering Validation • Probabilistic Classification • Decision Tree Classifier • Linear Discriminant Analysis • Support Vector Machines • Classification Assessment.

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