Predictive analytics and data mining: concepts and practice with RapidMiner
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston ; Heidelberg ; London
Elsevier/Morgan Kaufmann
[2015]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 2. Auflage ist erschienen unter dem Titel 'Data science - concepts and practice'. |
| Beschreibung: | XIX, 425 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780128014608 |
Internformat
MARC
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|---|---|---|---|
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| 020 | |a 9780128014608 |c pbk |9 978-0-12-801460-8 | ||
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| 100 | 1 | |a Kotu, Vijay |e Verfasser |0 (DE-588)1068240024 |4 aut | |
| 245 | 1 | 0 | |a Predictive analytics and data mining |b concepts and practice with RapidMiner |c Vijay Kotu, Bala Deshpande (PhD) |
| 264 | 1 | |a Amsterdam ; Boston ; Heidelberg ; London |b Elsevier/Morgan Kaufmann |c [2015] | |
| 300 | |a XIX, 425 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a 2. Auflage ist erschienen unter dem Titel 'Data science - concepts and practice'. | ||
| 630 | 0 | 4 | |a RapidMiner (Electronic resource) |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Forecasting / Statistical methods | |
| 650 | 4 | |a Business / Data processing | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Prognoseverfahren |0 (DE-588)4358095-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a RapidMiner |0 (DE-588)7755007-9 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 2 | |a RapidMiner |0 (DE-588)7755007-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Deshpande, Bala |e Verfasser |0 (DE-588)1068240210 |4 aut | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-027771810 | ||
Datensatz im Suchindex
| _version_ | 1804152942674051072 |
|---|---|
| adam_text | Contents
FOREWORD
...............................................................................................
XI
PREFACE
..................................................................................................
XV
ACKNOWLEDGEMENTS
...........................................................................XIX
CHAPTER
1
Introduction
........................................................1
1.1
What Data Mining Is
...............................................................................2
1.2
What Data Mining Is Not
........................................................................5
1.3
The Case for Data Mining
......................................................................6
1.4
Types of Data Mining
..............................................................................8
1.5
Data Mining Algorithms
.......................................................................10
1.6
Roadmap for Upcoming Chapters
.......................................................11
CHAPTER
2
Data Mining Process
.........................................17
2.1
Prior Knowledge
..................................................................................19
2.2
Data Preparation
..................................................................................22
2.3
Modeling
..............................................................................................27
2.4
Application
...........................................................................................32
2.5
Knowledge
...........................................................................................34
CHAPTER
3
Data Exploration
...............................................37
3.1
Objectives of Data Exploration
.............................................................38
3.2
Data Sets
..............................................................................................38
3.3
Descriptive Statistics
...........................................................................41
3.4
Data Visualization
................................................................................46
3.5
Roadmap for Data Exploration
............................................................59
vii
Contents
CHAPTER
4
Classification
....................................................63
4.1
Decision Trees
......................................................................................
M
4.2
Rule Induction
......................................................................................88
4.3
k-Nearest Neighbors
...........................................................................99
4.4
Naïve
Bayesian
...................................................................................
Ш
4.5
Artificial Neural Networks
.................................................................124
4.6
Support Vector Machines
...................................................................134
4.7
Ensemble Learners
...........................................................................148
CHAPTER
5
Regression Methods
.......................................165
5.1
Linear Regression
..............................................................................167
5.2
Logistic Regression
...........................................................................180
CHAPTER
6
Association Analysis
.......................................195
6.1
Concepts of Mining Association Rules
..............................................197
6.2
Apriori
Algorithm
...............................................................................202
6.3
FP-Growth Algorithm
........................................................................206
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7.1
Types of Clustering Techniques
.........................................................219
7.2
k-Means Clustering
...........................................................................223
7.3
DBSCAN Clustering
...........................................................................234
7.4
Self-Organizing Maps
........................................................................242
CHAPTER
8
Model Evaluation
............................................257
8.1
Confusion Matrix (or Truth Table)
......................................................258
8.2
Receiver Operator Characteristic (ROC) Curves and
Area under the Curve (AUC)
.................................... 260
8.3
Lift Curves
........................................ 263
8.4
Evaluating the Predictions: Implementation
.....................................264
CHAPTER
9
Text Mining
.....................................................275
*.1 How Text Mining Works
...................... 277
9.2
Implementing Text Mining with Clustering and Classification
.11284
Contents
CHAPTER
10
Time Series Forecasting
..............................305
10.1
Data-Driven Approaches
...................................................................308
10.2
Model-Driven Forecasting Methods
..................................................313
CHAPTER
11
Anomaly Detection
.......................................329
11.1
Anomaly Detection Concepts
.............................................................329
11.2
Distance-Based Outlier Detection
.....................................................
ЗЗД
11.3
Density-Based Outlier Detection
.......................................................338
11.4
Local Outlier Factor
...........................................................................341
CHAPTER
12
Feature Selection
.........................................347
12.1
Classifying Feature Selection Methods
.............................................348
12.2
Principal Component Analysis
..........................................................
ЗД9
12.3
Information Theory-Based Filtering for Numeric Data
....................358
12.4
Chi-Square-Based Filtering for Categorical Data
.............................360
12.5
Wrapper-Type Feature Selection
.......................................................363
CHAPTER
13
Getting Started with RapidMiner
..................371
13.1
User Interface and Terminology
........................................................372
13.2
Data Importing and Exporting Tools
..................................................377
13.3
Data Visualization Tools
.....................................................................382
13.4
Data Transformation Tools
................................................................386
13.5
Sampling and Missing Value Tools
....................................................392
13.6
Optimization Tools
.............................................................................396
COMPARISON OF DATA MINING ALGORITHMS
.........................................407
INDEX
.....................................................................................................417
ABOUT THE AUTHORS
.............................................................................425
|
| any_adam_object | 1 |
| author | Kotu, Vijay Deshpande, Bala |
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| author_role | aut aut |
| author_sort | Kotu, Vijay |
| author_variant | v k vk b d bd |
| building | Verbundindex |
| bvnumber | BV042335166 |
| classification_rvk | SK 850 ST 530 ST 270 |
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| dewey-full | 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.312 |
| dewey-search | 006.312 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| format | Book |
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| illustrated | Illustrated |
| indexdate | 2024-07-10T01:18:44Z |
| institution | BVB |
| isbn | 9780128014608 |
| language | English |
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| publishDate | 2015 |
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| spelling | Kotu, Vijay Verfasser (DE-588)1068240024 aut Predictive analytics and data mining concepts and practice with RapidMiner Vijay Kotu, Bala Deshpande (PhD) Amsterdam ; Boston ; Heidelberg ; London Elsevier/Morgan Kaufmann [2015] XIX, 425 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier 2. Auflage ist erschienen unter dem Titel 'Data science - concepts and practice'. RapidMiner (Electronic resource) Datenverarbeitung Wirtschaft Data mining Forecasting / Statistical methods Business / Data processing Data Mining (DE-588)4428654-5 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf RapidMiner (DE-588)7755007-9 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 s Data Mining (DE-588)4428654-5 s RapidMiner (DE-588)7755007-9 s DE-604 Deshpande, Bala Verfasser (DE-588)1068240210 aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027771810&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kotu, Vijay Deshpande, Bala Predictive analytics and data mining concepts and practice with RapidMiner RapidMiner (Electronic resource) Datenverarbeitung Wirtschaft Data mining Forecasting / Statistical methods Business / Data processing Data Mining (DE-588)4428654-5 gnd Prognoseverfahren (DE-588)4358095-6 gnd RapidMiner (DE-588)7755007-9 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4358095-6 (DE-588)7755007-9 |
| title | Predictive analytics and data mining concepts and practice with RapidMiner |
| title_auth | Predictive analytics and data mining concepts and practice with RapidMiner |
| title_exact_search | Predictive analytics and data mining concepts and practice with RapidMiner |
| title_full | Predictive analytics and data mining concepts and practice with RapidMiner Vijay Kotu, Bala Deshpande (PhD) |
| title_fullStr | Predictive analytics and data mining concepts and practice with RapidMiner Vijay Kotu, Bala Deshpande (PhD) |
| title_full_unstemmed | Predictive analytics and data mining concepts and practice with RapidMiner Vijay Kotu, Bala Deshpande (PhD) |
| title_short | Predictive analytics and data mining |
| title_sort | predictive analytics and data mining concepts and practice with rapidminer |
| title_sub | concepts and practice with RapidMiner |
| topic | RapidMiner (Electronic resource) Datenverarbeitung Wirtschaft Data mining Forecasting / Statistical methods Business / Data processing Data Mining (DE-588)4428654-5 gnd Prognoseverfahren (DE-588)4358095-6 gnd RapidMiner (DE-588)7755007-9 gnd |
| topic_facet | RapidMiner (Electronic resource) Datenverarbeitung Wirtschaft Data mining Forecasting / Statistical methods Business / Data processing Data Mining Prognoseverfahren RapidMiner |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027771810&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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