Data mining algorithms in C++: data patterns and algorithms for modern applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
[2018]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xiv, 286 Seiten Illustrationen |
| ISBN: | 9781484233146 |
Internformat
MARC
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| 245 | 1 | 0 | |a Data mining algorithms in C++ |b data patterns and algorithms for modern applications |c Timothy Masters |
| 264 | 1 | |a Berkeley, CA |b Apress |c [2018] | |
| 264 | 4 | |c © 2018 | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Table of Contents
About the Author........................................................ vii
About the Technical Reviewers......................................................ix
Introduction................................................................... xi
Chapter 1: Information and Entropy..................................................1
Entropy...........................................................................1
Entropy of a Continuous Random Variable........................................5
Partitioning a Continuous Variable for Entropy.................................5
An Example of Improving Entropy...............................................10
Joint and Conditional Entropy....................................................12
Code for Conditional Entropy..................................................16
Mutual Information...............................................................17
Fano’s Bound and Selection of Predictor Variables........................... 19
Confusion Matrices and Mutual Information.....................................21
Extending Fano’s Bound for Upper Limits.......................................23
Simple Algorithms for Mutual Information......................................27
The TESTJDIS Program........................................................ 34
Continuous Mutual Information.................................................. 36
The Parzen Window Method......................................................37
Adaptive Partitioning.........................................................45
The TEST_CON Program..........................................................60
Asymmetric Information Measures..................................................61
Uncertainty Reduction....................................................... 61
Transfer Entropy: Schreiber’s Information Transfer............................65
in
TABLE OF CONTENTS
Chapter 2: Screening for Relationships................................................ 75
Simple Screening Methods................................................................75
Univariate Screening............................................................. 76
Bivariate Screening.................................................................76
Forward Stepwise Selection..........................................................76
Forward Selection Preserving Subsets................................................77
Backward Stepwise Selection....................................................... 77
Criteria for a Relationship.............................................................77
Ordinary Correlation................................................................78
Nonparametric Correlation...........................................................79
Accommodating Simple Nonlinearity................................................. 82
Chi-Square and Cramer’s V...........................................................85
Mutual Information and Uncertainty Reduction........................................88
Multivariate Extensions.............................................................88
Permutation Tests.......................................................................89
A Modestly Rigorous Statement of the Procedure......................................89
A More Intuitive Approach...........................................................91
Serial Correlation Can Be Deadly....................................................93
Permutation Algorithms..............................................................93
Outline of the Permutation Test Algorithm...........................................94
Permutation Testing for Selection Bias..............................................95
Combinatorial^ Symmetric Cross Validation...............................................97
The CSCV Algorithm.................................................................102
An Example of CSCV 00S Testing.....................................................109
Univariate Screening for Relationships.................................................110
Three Simple Examples..............................................................114
Bivariate Screening for Relationships.................................................116
Stepwise Predictor Selection Using Mutual Information................................ 124
Maximizing Relevance While Minimizing Redundancy...................................125
Code for the Relevance Minus Redundancy Algorithm..................................128
IV
TABLE OF CONTENTS
An Example of Relevance Minus Redundancy..........................................132
A Superior Selection Algorithm for Binary Variables...............................136
FREL for High-Dimensionality, Small Size Datasets.................................. 141
Regularization.................................................................. 145
Interpreting Weights..............................................................146
Bootstrapping FREL................................................................146
Monte Carlo Permutation Tests of FREL.............................................147
General Statement of the FREL Algorithm......................................... 149
Multithreaded Code for FREL.......................................................153
Some FREL Examples................................................................164
Chapter 3: Displaying Relationship Anomalies...........................................167
Marginal Density Product.............................................................171
Actual Density.......................................................................171
Marginal Inconsistency...............................................................171
Mutual Information Contribution......................................................172
Code for Computing These Plots.......................................................173
Comments on Showing the Display......................................................183
Chapter 4: Fun with Eigenvectors................................................... 185
Eigenvalues and Eigenvectors.........................................................186
Principal Components (If You Really Must)............................................188
The Factor Structure Is More Interesting.............................................189
A Simple Example..................................................................190
Rotation Can Make Naming Easier...................................................192
Code for Eigenvectors and Rotation...................................................194
Eigenvectors of a Real Symmetric Matrix......................................... 194
Factor Structure of a Dataset.....................................................196
Varimax Rotation..................................................................199
Horn’s Algorithm for Determining Dimensionality......................................202
Code for the Modified Horn Algorithm..............................................203
v
TABLE OF CONTENTS
Clustering Variables in a Subspace.....................................................213
Code for Clustering Variables.......................................................217
Separating Individual from Common Variance.............................................221
Log Likelihood the Slow, Definitional Way..........................................228
Log Likelihood the Fast, Intelligent Way...........................................230
The Basic Expectation Maximization Algorithm.......................................232
Code for Basic Expectation Maximization............................................234
Accelerating the EM Algorithm......................................................237
Code for Quadratic Acceleration with DECME-2s.......................................241
Putting It All Together............................................................246
Thoughts on My Version of the Algorithm.............................................257
Measuring Coherence....................................................................257
Code for Tracking Coherence.........................................................260
Coherence in the Stock Market.......................................................264
Chapter 5: Using the DATAMINE Program.....................................................267
File/Read Data File....................................................................267
File/Exit..............................................................................268
Screen/Univariate Screen...............................................................268
Screen/Bivariate Screen................................................................269
Screen/Relevance Minus Redundancy......................................................271
Screen/FREL............................................................................272
Analyze/Eigen Analysis.................................................................274
Analyze/Factor Analysis................................................................274
Analyze/Rotate.........................................................................275
Analyze/Cluster Variables..............................................................276
Analyze/Coherence......................................................................276
Plot/Series............................................................................277
Plot/Histogram.........................................................................277
Plot/Density...........................................................................277
Index.....................................................................................281
vi
|
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| dewey-ones | 005 - Computer programming, programs, data, security |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| illustrated | Illustrated |
| indexdate | 2024-07-10T08:03:07Z |
| institution | BVB |
| isbn | 9781484233146 |
| language | English |
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| physical | xiv, 286 Seiten Illustrationen |
| publishDate | 2018 |
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| spelling | Masters, Timothy Verfasser (DE-588)1115733699 aut Data mining algorithms in C++ data patterns and algorithms for modern applications Timothy Masters Berkeley, CA Apress [2018] © 2018 xiv, 286 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier C++ (DE-588)4193909-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Algorithmus (DE-588)4001183-5 s C++ (DE-588)4193909-8 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4842-3315-3 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=030255360&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Masters, Timothy Data mining algorithms in C++ data patterns and algorithms for modern applications C++ (DE-588)4193909-8 gnd Data Mining (DE-588)4428654-5 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4193909-8 (DE-588)4428654-5 (DE-588)4001183-5 |
| title | Data mining algorithms in C++ data patterns and algorithms for modern applications |
| title_auth | Data mining algorithms in C++ data patterns and algorithms for modern applications |
| title_exact_search | Data mining algorithms in C++ data patterns and algorithms for modern applications |
| title_full | Data mining algorithms in C++ data patterns and algorithms for modern applications Timothy Masters |
| title_fullStr | Data mining algorithms in C++ data patterns and algorithms for modern applications Timothy Masters |
| title_full_unstemmed | Data mining algorithms in C++ data patterns and algorithms for modern applications Timothy Masters |
| title_short | Data mining algorithms in C++ |
| title_sort | data mining algorithms in c data patterns and algorithms for modern applications |
| title_sub | data patterns and algorithms for modern applications |
| topic | C++ (DE-588)4193909-8 gnd Data Mining (DE-588)4428654-5 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | C++ Data Mining Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030255360&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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