Data mining: theories, algorithms, and examples
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2017
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| Series: | Human factors and ergonomics series
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xix, 329 Seiten Diagramme |
| ISBN: | 9781138073661 |
Staff View
MARC
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Record in the Search Index
| _version_ | 1804177770656301056 |
|---|---|
| adam_text | Contents
Preface.......................................................xiii
Acknowledgments...............................................xvii
Author.........................................................xix
Part I An Overview of Data Mining
1. Introduction to Data, Data Patterns, and Data Mining...............3
1.1 Examples of Small Data Sets..................................3
1.2 Types of Data Variables......................................5
1.2.1 Attribute Variable versus Target Variable.............5
1.2.2 Categorical Variable versus Numeric Variable..........8
1.3 Data Patterns Learned through Data Mining....................9
1.3.1 Classification and Prediction Patterns.................9
1.3.2 Cluster and Association Patterns......................12
1.3.3 Data Reduction Patterns...............................13
1.3.4 Outlier and Anomaly Patterns..........................14
1.3.5 Sequential and Temporal Patterns......................15
1.4 Training Data and Test Data..................................17
Exercises..........................................................17
Part II Algorithms for Mining Classification
and Prediction Patterns
2. Linear and Nonlinear Regression Models.............................21
2.1 Linear Regression Models.....................................21
2.2 Least-Squares Method and Maximum Likelihood Method
of Parameter Estimation......................................23
2.3 Nonlinear Regression Models and Parameter Estimation.........28
2.4 Software and Applications....................................29
Exercises..........................................................29
3. Naïve Bayes Classifier.............................................31
3.1 Bayes Theorem................................................31
3.2 Classification Based on the Bayes Theorem and Naïve Bayes
Classifier...................................................31
3.3 Software and Applications....................................35
Exercises..........................................................36
vii
viii
Contents
4. Decision and Regression Trees....................................37
4.1 Learning a Binary Decision Tree and Classifying Data
Using a Decision Tree......................................37
4.1.1 Elements of a Decision Tree..........................37
4.1.2 Decision Tree with the Minimum Description Length....39
4.1.3 Split Selection Methods.............................40
4.1.4 Algorithm for the Top-Down Construction
of a Decision Tree..................................44
4.1.5 Classifying Data Using a Decision Tree..............49
4.2 Learning a Nonbinary Decision Tree........................51
4.3 Handling Numeric and Missing Values of Attribute Variables ..,.56
4.4 Handling a Numeric Target Variable and Constructing
a Regression Tree.........................................57
4.5 Advantages and Shortcomings of the Decision Tree
Algorithm............................................... 59
4.6 Software and Applications.................................61
Exercises.......................................................62
5. Artificial Neural Networks for Classification and Prediction....63
5.1 Processing Units of ANNs..................................63
5.2 Architectures of ANNs.....................................69
5.3 Methods of Determining Connection Weights for a Perceptron.71
5.3.1 Perceptron..........................................72
5.3.2 Properties of a Processing Unit................... 72
5.3.3 Graphical Method of Determining Connection
Weights and Biases..................................73
5.3.4 Learning Method of Determining Connection
Weights and Biases................................ 76
5.3.5 Limitation of a Perceptron..........................79
5.4 Back-Propagation Learning Method for a Multilayer
Feedforward ANN...........................................80
5.5 Empirical Selection of an ANN Architecture for a Good Fit
to Data...................................................86
5.6 Software and Applications.................................88
Exercises.......................................................88
6. Support Vector Machines.........................................91
6.1 Theoretical Foundation for Formulating and Solving an
Optimization Problem to Learn a Classification Function...91
6.2 SVM Formulation for a Linear Classifier and a Linearly
Separable Problem.........................................93
6.3 Geometric Interpretation of the SVM Formulation
for the Linear Classifier.................................96
6.4 Solution of the Quadratic Programming Problem
for a Linear Classifier
98
Contents 1X
6.5 SVM Formulation for a Linear Classifier and a Nonlinearly
Separable Problem.........................................105
6.6 SVM Formulation for a Nonlinear Classifier
and a Nonlinearly Separable Problem.......................108
6.7 Methods of Using SVM for Multi-Class Classification
Problems..................................................113
6.8 Comparison of ANN and SVM.................................113
6.9 Software and Applications.................................114
Exercises.......................................................114
7. fc-Nearest Neighbor Classifier and Supervised Clustering.......117
7.1 k-Nearest Neighbor Classifier.............................117
7.2 Supervised Clustering.....................................122
7.3 Software and Applications.................................136
Exercises.......................................................136
Part III Algorithms for Mining Cluster
and Association Patterns
8. Hierarchical Clustering........................................141
8.1 Procedure of Agglomerative Hierarchical Clustering........141
8.2 Methods of Determining the Distance between Two Clusters..141
8.3 Illustration of the Hierarchical Clustering Procedure.....146
8.4 Nonmonotonic Tree of Hierarchical Clustering..............150
8.5 Software and Applications.................................152
Exercises.......................................................152
9. K-Means Clustering and Density-Based Clustering................153
9.1 K-Means Clustering........................................153
9.2 Density-Based Clustering..................................165
9.3 Software and Applications.................................165
Exercises.......................................................166
10. Self-Organizing Map.............................................167
10.1 Algorithm of Self-Organizing Map..........................167
10.2 Software and Applications............................... 175
Exercises.......................................................175
11. Probability Distributions of Univariate Data....................177
11.1 Probability Distribution of Univariate Data and Probability
Distribution Characteristics of Various Data Patterns.....177
11.2 Method of Distinguishing Four Probability Distributions...182
11.3 Software and Applications.................................183
Exercises.......................................................184
X 1 Contents
12. Association Rules.............................................I85
12.1 Definition of Association Rules and Measures of Association.I85
12.2 Association Rule Discovery...............................lgg
12.3 Software and Applications................................I94
Exercises.....................................................I94
13. Bayesian Network..............................................197
13.1 Structure of a Bayesian Network and Probability
Distributions of Variables..................................197
13.2 Probabilistic Inference..................................205
13.3 Learning of a Bayesian Network...........................210
13.4 Software and Applications................................213
Exercises.....................................................213
Part IV Algorithms for Mining Data Reduction Patterns
14. Principal Component Analysis.....................................217
14.1 Review of Multivariate Statistics...................... 217
14.2 Review of Matrix Algebra.................................220
14.3 Principal Component Analysis.............................228
14.4 Software and Applications............................... 230
Exercises................................................... 231
15. Multidimensional Scaling......................................233
15.1 Algorithm of MDS....................................... 233
15.2 Number of Dimensions.....................................246
15.3 INDSCALE for Weighted MDS................................247
15.4 Software and Applications................................248
Exercises.....................................................248
Part V Algorithms for Mining Outlier
and Anomaly Patterns
16. Univariate Control Charts.....................................251
16.1 Shewhart Control Charts..................................251
16.2 CUSUM Control Charts.....................................254
16.3 EWMA Control Charts.................................... 257
16.4 Cuscore Control Charts...................................261
16.5 Receiver Operating Curve (ROC) for Evaluation
and Comparison of Control Charts.........................265
16.6 Software and Applications................................267
Exercises.....................................................267
Contents X1
17. Multivariate Control Charts....................................269
17.1 Hotelling s T2 Control Charts.............................269
17.2 Multivariate EWMA Control Charts..........................272
17.3 Chi-Square Control Charts.................................272
17.4 Applications..............................................274
Exercises.................................................... 274
Part VI Algorithms for Mining Sequential
and Temporal Patterns
18. Autocorrelation and Time Series Analysis.......................277
18.1 Autocorrelation...........................................277
18.2 Stationarity and Nonstationarity..........................278
18.3 ARMA Models of Stationary Series Data.....................279
18.4 ACF and PACF Characteristics of ARMA Models........... 281
18.5 Transformations of Nonstationary Series Data
and ARIMA Models..........................................283
18.6 Software and Applications.................................284
Exercises......................................................285
19. Markov Chain Models and Hidden Markov Models...................287
19.1 Markov Chain Models.......................................287
19.2 Hidden Markov Models......................................290
19.3 Learning Hidden Markov Models.............................294
19.4 Software and Applications.................................305
Exercises......................................................305
20. Wavelet Analysis...............................................307
20.1 Definition of Wavelet.....................................307
20.2 Wavelet Transform of Time Series Data.....................309
20.3 Reconstruction of Time Series Data from Wavelet
Coefficients..............................................316
20.4 Software and Applications.................................317
Exercises......................................................318
References..........................................................319
Index...............................................................323
|
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| indexdate | 2024-07-10T07:53:22Z |
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| isbn | 9781138073661 |
| language | English |
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| physical | xix, 329 Seiten Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
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| publisher | CRC Press, Taylor & Francis Group |
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| spelling | Ye, Nong Verfasser (DE-588)136994121 aut Data mining theories, algorithms, and examples Nong Ye Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2017 xix, 329 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Human factors and ergonomics series Includes bibliographical references and index Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029853993&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ye, Nong Data mining theories, algorithms, and examples Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Data mining theories, algorithms, and examples |
| title_auth | Data mining theories, algorithms, and examples |
| title_exact_search | Data mining theories, algorithms, and examples |
| title_full | Data mining theories, algorithms, and examples Nong Ye |
| title_fullStr | Data mining theories, algorithms, and examples Nong Ye |
| title_full_unstemmed | Data mining theories, algorithms, and examples Nong Ye |
| title_short | Data mining |
| title_sort | data mining theories algorithms and examples |
| title_sub | theories, algorithms, and examples |
| topic | Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining |
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