### Editorial Reviews

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

### Book Details

**Author:**Nong Ye**Pages:**349 pages**Edition:**1**Publication Date:**2013-07-26**Publisher:**CRC Press**Language:**English**ISBN-10:**1439808384**ISBN-13:**9781439808382

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