Data Mining: Theories, Algorithms, and Examples

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms.

The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures.

The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

Table of Contents

Part I: An Overview of Data Mining
Chapter 1: Introduction to Data, Data Patterns, and Data Mining

Part II: Algorithms for Mining Classification and Prediction Patterns
Chapter 2: Linear and Nonlinear Regression Models
Chapter 3: Naïve Bayes Classifier
Chapter 4: Decision and Regression Trees
Chapter 5: Artificial Neural Networks for Classification and Prediction
Chapter 6: Support Vector Machines
Chapter 7: k-Nearest Neighbor Classifier and Supervised Clustering

Part III: Algorithms for Mining Cluster and Association Patterns
Chapter 8: Hierarchical Clustering
Chapter 9: K-Means Clustering and Density-Based Clustering
Chapter 10: Self-Organizing Map
Chapter 11: Probability Distributions of Univariate Data
Chapter 12: Association Rules
Chapter 13: Bayesian Network

Part IV: Algorithms for Mining Data Reduction Patterns
Chapter 14: Principal Component Analysis
Chapter 15: Multidimensional Scaling

Part V: Algorithms for Mining Outlier and Anomaly Patterns
Chapter 16: Univariate Control Charts
Chapter 17: Multivariate Control Charts

Part VI: Algorithms for Mining Sequential and Temporal Patterns
Chapter 18: Autocorrelation and Time Series Analysis
Chapter 19: Markov Chain Models and Hidden Markov Models
Chapter 20: Wavelet Analysis