book

Data Mining

Name: Data Mining
Author: Nong Ye
ISBN: 9781482219388

by Nong Ye

July 2013

Intermediate to advanced

349 pages

6h 33m

English

CRC Press

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Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Author
Part I An Overview of Data Mining
1. Introduction to Data, Data Patterns, and Data Mining
1.1 Examples of Small Data Sets1.2 Types of Data Variables1.2.1 Attribute Variable versus Target Variable1.2.2 Categorical Variable versus Numeric Variable1.3 Data Patterns Learned through Data Mining1.3.1 Classification and Prediction Patterns1.3.2 Cluster and Association Patterns1.3.3 Data Reduction Patterns1.3.4 Outlier and Anomaly Patterns1.3.5 Sequential and Temporal Patterns1.4 Training Data and Test DataExercises

Part II Algorithms for Mining Classification and Prediction Patterns
2. Linear and Nonlinear Regression Models
2.1 Linear Regression Models2.2 Least-Squares Method and Maximum Likelihood Method of Parameter Estimation2.3 Nonlinear Regression Models and Parameter Estimation2.4 Software and ApplicationsExercises
3. Naïve Bayes Classifier
3.1 Bayes Theorem3.2 Classification Based on the Bayes Theorem and Naïve Bayes Classifier3.3 Software and ApplicationsExercises
4. Decision and Regression Trees
4.1 Learning a Binary Decision Tree and Classifying Data Using a Decision Tree4.1.1 Elements of a Decision Tree4.1.2 Decision Tree with the Minimum Description Length4.1.3 Split Selection Methods4.1.4 Algorithm for the Top-Down Construction of a Decision Tree4.1.5 Classifying Data Using a Decision Tree4.2 Learning a Nonbinary Decision Tree4.3 Handling Numeric and Missing Values of Attribute Variables4.4 Handling a Numeric Target Variable and Constructing a Regression Tree4.5 Advantages and Shortcomings of the Decision Tree Algorithm4.6 Software and ApplicationsExercises
5. Artificial Neural Networks for Classification and Prediction
5.1 Processing Units of ANNs5.2 Architectures of ANNs5.3 Methods of Determining Connection Weights for a Perceptron5.3.1 Perceptron5.3.2 Properties of a Processing Unit5.3.3 Graphical Method of Determining Connection Weights and Biases5.3.4 Learning Method of Determining Connection Weights and Biases5.3.5 Limitation of a Perceptron5.4 Back-Propagation Learning Method for a Multilayer Feedforward ANN5.5 Empirical Selection of an ANN Architecture for a Good Fit to Data5.6 Software and ApplicationsExercises
6. Support Vector Machines
6.1 Theoretical Foundation for Formulating and Solving an Optimization Problem to Learn a Classification Function6.2 SVM Formulation for a Linear Classifier and a Linearly Separable Problem6.3 Geometric Interpretation of the SVM Formulation for the Linear Classifier6.4 Solution of the Quadratic Programming Problem for a Linear Classifier6.5 SVM Formulation for a Linear Classifier and a Nonlinearly Separable Problem6.6 SVM Formulation for a Nonlinear Classifier and a Nonlinearly Separable Problem6.7 Methods of Using SVM for Multi-Class Classification Problems6.8 Comparison of ANN and SVM6.9 Software and ApplicationsExercises
7. k-Nearest Neighbor Classifier and Supervised Clustering
7.1 k-Nearest Neighbor Classifier7.2 Supervised Clustering7.3 Software and ApplicationsExercises
Part III Algorithms for Mining Cluster and Association Patterns
8. Hierarchical Clustering
8.1 Procedure of Agglomerative Hierarchical Clustering8.2 Methods of Determining the Distance between Two Clusters8.3 Illustration of the Hierarchical Clustering Procedure8.4 Nonmonotonic Tree of Hierarchical Clustering8.5 Software and ApplicationsExercises
9. K-Means Clustering and Density-Based Clustering
9.1 K-Means Clustering9.2 Density-Based Clustering9.3 Software and ApplicationsExercises
10. Self-Organizing Map
10.1 Algorithm of Self-Organizing Map10.2 Software and ApplicationsExercises
11. Probability Distributions of Univariate Data
11.1 Probability Distribution of Univariate Data and Probability Distribution Characteristics of Various Data Patterns11.2 Method of Distinguishing Four Probability Distributions11.3 Software and ApplicationsExercises
12. Association Rules
12.1 Definition of Association Rules and Measures of Association12.2 Association Rule Discovery12.3 Software and ApplicationsExercises
13. Bayesian Network
13.1 Structure of a Bayesian Network and Probability Distributions of Variables13.2 Probabilistic Inference13.3 Learning of a Bayesian Network13.4 Software and ApplicationsExercises
Part IV Algorithms for Mining Data Reduction Patterns
14. Principal Component Analysis
14.1 Review of Multivariate Statistics14.2 Review of Matrix Algebra14.3 Principal Component Analysis14.4 Software and ApplicationsExercises
15. Multidimensional Scaling
15.1 Algorithm of MDS15.2 Number of Dimensions15.3 INDSCALE for Weighted MDS15.4 Software and ApplicationsExercises
Part V Algorithms for Mining Outlier and Anomaly Patterns
16. Univariate Control Charts
16.1 Shewhart Control Charts16.2 CUSUM Control Charts16.3 EWMA Control Charts16.4 Cuscore Control Charts16.5 Receiver Operating Curve (ROC) for Evaluation and Comparison of Control Charts16.6 Software and ApplicationsExercises
17. Multivariate Control Charts
17.1 Hotelling’s T2 Control Charts17.2 Multivariate EWMA Control Charts17.3 Chi-Square Control Charts17.4 ApplicationsExercises
Part VI Algorithms for Mining Sequential and Temporal Patterns
18. Autocorrelation and Time Series Analysis
18.1 Autocorrelation18.2 Stationarity and Nonstationarity18.3 ARMA Models of Stationary Series Data18.4 ACF and PACF Characteristics of ARMA Models18.5 Transformations of Nonstationary Series Data and ARIMA Models18.6 Software and ApplicationsExercises
19. Markov Chain Models and Hidden Markov Models
19.1 Markov Chain Models19.2 Hidden Markov Models19.3 Learning Hidden Markov Models19.4 Software and ApplicationsExercises
20. Wavelet Analysis
20.1 Definition of Wavelet20.2 Wavelet Transform of Time Series Data20.3 Reconstruction of Time Series Data from Wavelet Coefficients20.4 Software and ApplicationsExercises
References
Index

Content preview from Data Mining

15 Multidimensional Scaling

Multidimensional scaling (MDS) aims at representing high-dimensional data in a low-dimensional space so that data can be visualized, analyzed, and interpreted in the low-dimensional space to uncover useful data patterns. This chapter describes MDS, software packages supporting MDS, and some applications of MDS with references.

15.1 Algorithm of MDS

We are given n data items in the p-dimensional space, x_i = (x_i1, …, x_ip), i = 1, …, n, along with the dissimilarity δ_ij of each pair of n data items, x_i and x_j, and the rank order of these dissimilarities from the least similar pair to the most similar pair:

$eq511.jpg$

(15.1)

where M denotes the total number of different data pairs, and M = n(n

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ISBN: 9781482219364

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Data Mining

by Nong Ye

15

Multidimensional Scaling

15.1 Algorithm of MDS

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