ABSTRACT

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 dat

part Part I|18 pages

An Overview of Data Mining

part Part II|119 pages

Algorithms for Mining Classification and Prediction Patterns

chapter 2|9 pages

Linear and Nonlinear Regression Models

chapter 3|6 pages

Naïve Bayes Classifier

chapter 4|26 pages

Decision and Regression Trees

chapter 6|25 pages

Support Vector Machines

part Part III|75 pages

Algorithms for Mining Cluster and Association Patterns

chapter 8|12 pages

Hierarchical Clustering

chapter 10|10 pages

Self-Organizing Map

chapter 12|11 pages

Association Rules

chapter 13|17 pages

Bayesian Network

part Part IV|34 pages

Algorithms for Mining Data Reduction Patterns

chapter 14|15 pages

Principal Component Analysis

chapter 15|16 pages

Multidimensional Scaling

part Part V|26 pages

Algorithms for Mining Outlier and Anomaly Patterns

chapter 16|17 pages

Univariate Control Charts

chapter 17|6 pages

Multivariate Control Charts

part Part VI|44 pages

Algorithms for Mining Sequential and Temporal Patterns

chapter 18|9 pages

Autocorrelation and Time Series Analysis

chapter 19|19 pages

Markov Chain Models and Hidden Markov Models

chapter 20|12 pages

Wavelet Analysis