Data Mining with SPSS Modeler: Theory, Exercises and Solutions Volume 2 Volume 2
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| Language: | English |
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Springer International Publishing
2021
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| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVII, 547-1274 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030543372 |
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| adam_text | Contents 6 Factor Analysis............................................................................................ 6.1 Motivating Example......................................................................... 6.2 General Theory of Factor Analysis................................................. 6.3 Principal Component Analysis........................................................ 6.3.1 Theory............................................................................... 6.3.2 Building a Model in SPSS Modeler............................... 6.3.3 Exercises........................................................................... 6.3.4 Solutions........................................................................... 6.4 Principal Factor Analysis................................................................. 6.4.1 Theory............................................................................... 6.4.2 Building a Model.............................................................. 6.4.3 Feature Selection vs. Feature Reduction........................ 6.4.4 Exercises........................................................................... 6.4.5 Solutions............................................................................ References..................................................................................................... 547 547 549 552 552 553 580 583 605 605 607 613 616 617 621 7 Cluster Analysis......................................... 7.1 Motivating Examples....................................................................... 7.2 General
Theory of Cluster Analysis................................................ 7.2.1 Exercises........................................................................... 7.2.2 Solutions............................................................................ 7.3 TwoStep Hierarchical Agglomerative Clustering.......................... 7.3.1 Theory of Hierarchical Clustering................................... 7.3.2 Characteristics of the TwoStep Algorithm..................... 7.3.3 Building a Model in SPSS Modeler................................ 7.3.4 Exercises............................................................................ 7.3.5 Solutions............................................................................ 7.4 К-Means Partitioning Clustering..................................................... 7.4.1 Theory............................................................................... 7.4.2 Building a Model in SPSS Modeler................................ 7.4.3 Exercises............................................................................ 7.4.4 Solutions............................................................................ 7.5 Auto Clustering................................................................................. 623 623 625 632 634 637 637 650 652 664 665 676 676 678 696 698 724
x Contents 7.5.1 Motivation and Implementation of the Auto Cluster Node................................................... 724 7.5.2 Building a Model in SPSS Modeler............................... 7.5.3 Exercises.......................................................................... 7.5.4 Solutions.......................................................................... 7.6 Summary.......................................................................................... References................................................................................................... 8 Classification Models................................................................................ 8.1 Motivating Examples....................................................................... 8.2 General Theory of Classification Models....................................... 8.2.1 Process of Training and Using a Classification Model.............................................................. 8.2.2 Classification Algorithms................................................ 8.2.3 Classification Versus Clustering..................................... 8.2.4 Decision Boundary and the Problem with Over- and Underfitting................................... 763 8.2.5 Performance Measures of Classification Models............ 8.2.6 The Analysis Node........................................................... 8.2.7 The Evaluation Node........................................................ 8.2.8 A Detailed Example how to Create a ROC Curve.... 8.2.9
Exercises............................................................................ 8.2.10 Solutions............................................................................ 8.3 Logistic Regression.......................................................................... 8.3.1 Theory............................................................................... 8.3.2 Building the Model in SPSS Modeler............................. 8.3.3 Optional: Model Types and Variable Interactions ... . 8.3.4 Final Model and Its Goodness of Fit.............................. 8.3.5 Classification of Unknown Values.................................. 8.3.6 Cross-Validation of the Model........................................ 8.3.7 Exercises........................................................................... 8.3.8 Solutions........................................................................... 8.4 Linear Discriminate Classification.................................................. 8.4.1 Theory............................................................................... 8.4.2 Building the Model with SPSS Modeler........................ 8.4.3 The Model Nugget and the Estimated Model Parameters....................................................................... 8.4.4 Exercises........................................................................... 8.4.5 Solutions........................................................................... 8.5 Support Vector Machine.................................................................. 8.5.1
Theory............................................................................... 8.5.2 Building the Model with SPSS Modeler........................ 8.5.3 The Model Nugget........................................................... 8.5.4 Exercises........................................................................... 8.5.5 Solutions........................................................................... 726 738 739 750 751 753 754 756 756 758 760 765 769 771 780 794 798 804 805 808 816 818 825 825 828 833 851 851 857 863 868 871 896 896 899 911 911 913
Contents xi Neuronal Networks.......................................................................... 935 8.6.1 Theory............................................................................... 937 8.6.2 Building a Network with SPSS Modeler....................... 940 8.6.3 The Model Nugget........................................................... 949 8.6.4 Exercises........................................................................... 955 8.6.5 Solutions............................................................................ 957 8.7 К-Nearest Neighbor......................................................................... 974 8.7.1 Theory............................................................................... 974 8.7.2 Building the Model with SPSS Modeler........................ 978 8.7.3 The Model Nugget........................................................... 988 8.7.4 Dimensional Reduction with PCA for Data Preprocessing................................................. 991 8.7.5 Exercises............................................................................ 998 8.7.6 Solutions............................................................................ 1001 8.8 Decision Trees.................................................................................. 1015 8.8.1 Theory............................................................................... 1016 8.8.2 Building a Decision Tree withthe C5.0 Node................ 1024 8.8.3 The Model Nugget........................................................... 1028 8.8.4 Building a Decision Tree
withthe CHAID Node.... 1031 8.8.5 Exercises............................................................................ 1037 8.8.6 Solutions............................................................................ 1040 8.9 The Auto Classifier Node................................................................ 1061 8.9.1 Building a Stream with the Auto Classifier Node... . 1063 8.9.2 The Auto Classifier Model Nugget................................. 1073 8.9.3 Exercises............................................................................ 1076 8.9.4 Solutions............................................................................ 1076 References.................................................................................................... 1086 8.6 9 10 Using R with the Modeler......................................................................... 9.1 Advantages of R with the Modeler................................................. 9.2 Connecting with R............................................................................ 9.3 Test the SPSS Modeler Connection to R........................................ 9.4 Calculating New Variables in R...................................................... 9.5 Model Building in R........................................................................ 9.6 Modifying the Data Structure in R.................................................. 9.7 Solutions...........................................................................................
References.................................................................................................... 1089 1089 1090 1094 1098 1103 1114 ИЗО 1146 Imbalanced Data and Resampling Techniques.................................. 10.1 Characteristics of Imbalanced Datasets and Consequences.... 10.2 Resampling Techniques................................................................. 10.2.1 Random Oversampling Examples (ROSE)................. 10.2.2 Synthetic Minority Oversampling Technique (SMOTE)....................................................... 10.2.3 Adaptive Synthetic Sampling Method (abbr. ADASYN)..................................................... 1147 1147 1150 1153 1153 1156
xii Contents Implementation in SPSS Modeler................................................ Using R to Implement Balancing Methods................................ 10.4.1 SMOTE-Approach Using R......................................... 10.4.2 ROSE-Approach Using R............................................ 10.5 Exercises........................................................................................ 10.5.1 Exercise 1 : Recap Imbalanced Data............................ 10.5.2 Exercise 2: Resampling Application to Identify Cancer........................................................... 1183 10.5.3 Exercise 3: Comparing Resampling Algorithms ... . 10.6 Solutions......................................................................................... 10.6.1 Exercise 1: Recap Imbalanced Data............................. 10.6.2 Exercise 2: Resampling Application to Identify Cancer........................................................... 1188 10.6.3 Exercise 3: Comparing Resampling Algorithms. ... References.................................................................................................. 10.3 10.4 1157 1170 1170 1176 1182 1182 1186 1187 1187 1189 1190 11 Case Study: Fault Detection in Semiconductor Manufacturing Process............................................................................................... 1193 11.1 Case Study Background................................................................ 1193 11.2 The Standard Process in Data Mining.......................................... 1194 11.2.1 Business Understanding (CRISP-DM Step
1)............ 1195 11.2.2 Data Understanding (CRISP-DM Step 2).................... 1195 11.2.3 Data Preparation (CRISP-DM Step 3)......................... 1197 11.2.4 Modeling (CRISP-DMStep 4)...................................... 1234 11.2.5 Evaluation and Deployment of Model (CRISP-DMStep5and 6).............................. 1241 11.3 Lessons Learned............................................................................ 1243 11.4 Exercises........................................................................................ 1245 11.5 Solutions........................................................................................ 1246 References.................................................................................................. 1247 12 Appendix................................................................................................... 12.1 Data Sets Used in This Book........................................................ 12.1.1 adult_income_data.txt.................................................. 12.1.2 bank_full.csv................................................................. 12.1.3 beer.sav......................................................................... 12.1.4 benchmark.xlsx............................................................. 12.1.5 car_simple.sav.............................................................. 12.1.6 car_sales_modified.sav................................................ 12.1.7 chess_endgame_data.txt............................................... 12.1.8 credit_card_sampling_data.sav
................................... 12.1.9 customer_bank_data.csv.............................................. 12.1.10 diabetes_data_reduced.sav.......................................... 12.1.11 DRUGI n.csv................................................................ 12.1.12 EEG_Sleep_Signals.csv.............................................. 1249 1249 1249 1249 1251 1251 1251 1252 1252 1253 1253 1253 1254 1255
Contents xiii employee_dataset_001 and employee_dataset_002. . . England Payment Datasets........................................... Features_eeg_signals.csv............................................. gene_expression_leukemia_all.csv.............................. gene_expression_leukemia_short.csv......................... gravity_constant_data.csv............................................ hacide_train.SAV and hacide_test.SAV..................... Housing.data.txt............................................................ income_vs_purchase.sav.............................................. Iris.csv............................................................................ IT-projects.txt................................................................ IT user satisfaction.sav................................................. longley.csv..................................................................... LPGA2009.CSV.............................................................. Mtcars.csv...................................................................... nutrition_habites.sav..................................................... optdigits_training.txt, optdigits_test.txt...................... Orthodont.csv................................................................ Ozone.csv...................................................................... pisa2012_math_q45.sav............................................... sales_list.sav..................................................................
secom.sav....................................................................... ships.csv......................................................................... test_scores.sav............................................................... Titanic.xlsx.................................................................... tree_credit.sav................................................................ wine_data.txt.................................................................. WisconsinBreastCancerData.csv and wisconsin_breast_cancer_data.sav............................... 12.1.41 z_pm_customerl .sav..................................................... References.................................................................................................. 12.1.13 12.1.14 12.1.15 12.1.16 12.1.17 12.1.18 12.1.19 12.1.20 12.1.21 12.1.22 12.1.23 12.1.24 12.1.25 12.1.26 12.1.27 12.1.28 12.1.29 12.1.30 12.1.31 12.1.32 12.1.33 12.1.34 12.1.35 12.1.36 12.1.37 12.1.38 12.1.39 12.1.40 1255 1255 1257 1257 1258 1258 1259 1259 1259 1259 1260 1260 1261 1261 1261 1261 1265 1265 1265 1266 1266 1267 1268 1269 1269 1269 1270 1270 1271 1272
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| adam_txt |
Contents 6 Factor Analysis. 6.1 Motivating Example. 6.2 General Theory of Factor Analysis. 6.3 Principal Component Analysis. 6.3.1 Theory. 6.3.2 Building a Model in SPSS Modeler. 6.3.3 Exercises. 6.3.4 Solutions. 6.4 Principal Factor Analysis. 6.4.1 Theory. 6.4.2 Building a Model. 6.4.3 Feature Selection vs. Feature Reduction. 6.4.4 Exercises. 6.4.5 Solutions. References. 547 547 549 552 552 553 580 583 605 605 607 613 616 617 621 7 Cluster Analysis. 7.1 Motivating Examples. 7.2 General
Theory of Cluster Analysis. 7.2.1 Exercises. 7.2.2 Solutions. 7.3 TwoStep Hierarchical Agglomerative Clustering. 7.3.1 Theory of Hierarchical Clustering. 7.3.2 Characteristics of the TwoStep Algorithm. 7.3.3 Building a Model in SPSS Modeler. 7.3.4 Exercises. 7.3.5 Solutions. 7.4 К-Means Partitioning Clustering. 7.4.1 Theory. 7.4.2 Building a Model in SPSS Modeler. 7.4.3 Exercises. 7.4.4 Solutions. 7.5 Auto Clustering. 623 623 625 632 634 637 637 650 652 664 665 676 676 678 696 698 724
x Contents 7.5.1 Motivation and Implementation of the Auto Cluster Node. 724 7.5.2 Building a Model in SPSS Modeler. 7.5.3 Exercises. 7.5.4 Solutions. 7.6 Summary. References. 8 Classification Models. 8.1 Motivating Examples. 8.2 General Theory of Classification Models. 8.2.1 Process of Training and Using a Classification Model. 8.2.2 Classification Algorithms. 8.2.3 Classification Versus Clustering. 8.2.4 Decision Boundary and the Problem with Over- and Underfitting. 763 8.2.5 Performance Measures of Classification Models. 8.2.6 The Analysis Node. 8.2.7 The Evaluation Node. 8.2.8 A Detailed Example how to Create a ROC Curve. 8.2.9
Exercises. 8.2.10 Solutions. 8.3 Logistic Regression. 8.3.1 Theory. 8.3.2 Building the Model in SPSS Modeler. 8.3.3 Optional: Model Types and Variable Interactions . . 8.3.4 Final Model and Its Goodness of Fit. 8.3.5 Classification of Unknown Values. 8.3.6 Cross-Validation of the Model. 8.3.7 Exercises. 8.3.8 Solutions. 8.4 Linear Discriminate Classification. 8.4.1 Theory. 8.4.2 Building the Model with SPSS Modeler. 8.4.3 The Model Nugget and the Estimated Model Parameters. 8.4.4 Exercises. 8.4.5 Solutions. 8.5 Support Vector Machine. 8.5.1
Theory. 8.5.2 Building the Model with SPSS Modeler. 8.5.3 The Model Nugget. 8.5.4 Exercises. 8.5.5 Solutions. 726 738 739 750 751 753 754 756 756 758 760 765 769 771 780 794 798 804 805 808 816 818 825 825 828 833 851 851 857 863 868 871 896 896 899 911 911 913
Contents xi Neuronal Networks. 935 8.6.1 Theory. 937 8.6.2 Building a Network with SPSS Modeler. 940 8.6.3 The Model Nugget. 949 8.6.4 Exercises. 955 8.6.5 Solutions. 957 8.7 К-Nearest Neighbor. 974 8.7.1 Theory. 974 8.7.2 Building the Model with SPSS Modeler. 978 8.7.3 The Model Nugget. 988 8.7.4 Dimensional Reduction with PCA for Data Preprocessing. 991 8.7.5 Exercises. 998 8.7.6 Solutions. 1001 8.8 Decision Trees. 1015 8.8.1 Theory. 1016 8.8.2 Building a Decision Tree withthe C5.0 Node. 1024 8.8.3 The Model Nugget. 1028 8.8.4 Building a Decision Tree
withthe CHAID Node. 1031 8.8.5 Exercises. 1037 8.8.6 Solutions. 1040 8.9 The Auto Classifier Node. 1061 8.9.1 Building a Stream with the Auto Classifier Node. . 1063 8.9.2 The Auto Classifier Model Nugget. 1073 8.9.3 Exercises. 1076 8.9.4 Solutions. 1076 References. 1086 8.6 9 10 Using R with the Modeler. 9.1 Advantages of R with the Modeler. 9.2 Connecting with R. 9.3 Test the SPSS Modeler Connection to R. 9.4 Calculating New Variables in R. 9.5 Model Building in R. 9.6 Modifying the Data Structure in R. 9.7 Solutions.
References. 1089 1089 1090 1094 1098 1103 1114 ИЗО 1146 Imbalanced Data and Resampling Techniques. 10.1 Characteristics of Imbalanced Datasets and Consequences. 10.2 Resampling Techniques. 10.2.1 Random Oversampling Examples (ROSE). 10.2.2 Synthetic Minority Oversampling Technique (SMOTE). 10.2.3 Adaptive Synthetic Sampling Method (abbr. ADASYN). 1147 1147 1150 1153 1153 1156
xii Contents Implementation in SPSS Modeler. Using R to Implement Balancing Methods. 10.4.1 SMOTE-Approach Using R. 10.4.2 ROSE-Approach Using R. 10.5 Exercises. 10.5.1 Exercise 1 : Recap Imbalanced Data. 10.5.2 Exercise 2: Resampling Application to Identify Cancer. 1183 10.5.3 Exercise 3: Comparing Resampling Algorithms . . 10.6 Solutions. 10.6.1 Exercise 1: Recap Imbalanced Data. 10.6.2 Exercise 2: Resampling Application to Identify Cancer. 1188 10.6.3 Exercise 3: Comparing Resampling Algorithms. . References. 10.3 10.4 1157 1170 1170 1176 1182 1182 1186 1187 1187 1189 1190 11 Case Study: Fault Detection in Semiconductor Manufacturing Process. 1193 11.1 Case Study Background. 1193 11.2 The Standard Process in Data Mining. 1194 11.2.1 Business Understanding (CRISP-DM Step
1). 1195 11.2.2 Data Understanding (CRISP-DM Step 2). 1195 11.2.3 Data Preparation (CRISP-DM Step 3). 1197 11.2.4 Modeling (CRISP-DMStep 4). 1234 11.2.5 Evaluation and Deployment of Model (CRISP-DMStep5and 6). 1241 11.3 Lessons Learned. 1243 11.4 Exercises. 1245 11.5 Solutions. 1246 References. 1247 12 Appendix. 12.1 Data Sets Used in This Book. 12.1.1 adult_income_data.txt. 12.1.2 bank_full.csv. 12.1.3 beer.sav. 12.1.4 benchmark.xlsx. 12.1.5 car_simple.sav. 12.1.6 car_sales_modified.sav. 12.1.7 chess_endgame_data.txt. 12.1.8 credit_card_sampling_data.sav
. 12.1.9 customer_bank_data.csv. 12.1.10 diabetes_data_reduced.sav. 12.1.11 DRUGI n.csv. 12.1.12 EEG_Sleep_Signals.csv. 1249 1249 1249 1249 1251 1251 1251 1252 1252 1253 1253 1253 1254 1255
Contents xiii employee_dataset_001 and employee_dataset_002. . . England Payment Datasets. Features_eeg_signals.csv. gene_expression_leukemia_all.csv. gene_expression_leukemia_short.csv. gravity_constant_data.csv. hacide_train.SAV and hacide_test.SAV. Housing.data.txt. income_vs_purchase.sav. Iris.csv. IT-projects.txt. IT user satisfaction.sav. longley.csv. LPGA2009.CSV. Mtcars.csv. nutrition_habites.sav. optdigits_training.txt, optdigits_test.txt. Orthodont.csv. Ozone.csv. pisa2012_math_q45.sav. sales_list.sav.
secom.sav. ships.csv. test_scores.sav. Titanic.xlsx. tree_credit.sav. wine_data.txt. WisconsinBreastCancerData.csv and wisconsin_breast_cancer_data.sav. 12.1.41 z_pm_customerl .sav. References. 12.1.13 12.1.14 12.1.15 12.1.16 12.1.17 12.1.18 12.1.19 12.1.20 12.1.21 12.1.22 12.1.23 12.1.24 12.1.25 12.1.26 12.1.27 12.1.28 12.1.29 12.1.30 12.1.31 12.1.32 12.1.33 12.1.34 12.1.35 12.1.36 12.1.37 12.1.38 12.1.39 12.1.40 1255 1255 1257 1257 1258 1258 1259 1259 1259 1259 1260 1260 1261 1261 1261 1261 1265 1265 1265 1266 1266 1267 1268 1269 1269 1269 1270 1270 1271 1272 |
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| index_date | 2024-07-03T20:07:36Z |
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| oclc_num | 1334050019 |
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| owner | DE-473 DE-BY-UBG DE-573 |
| owner_facet | DE-473 DE-BY-UBG DE-573 |
| physical | XVII, 547-1274 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Springer International Publishing |
| record_format | marc |
| spelling | Wendler, Tilo Verfasser (DE-588)1106403002 aut Data Mining with SPSS Modeler Theory, Exercises and Solutions Volume 2 Volume 2 Tilo Wendler ; Sören Gröttrup Cham Springer International Publishing 2021 XVII, 547-1274 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier SPSS (DE-588)4056588-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Statistik (DE-588)4056995-0 s Data Mining (DE-588)4428654-5 s SPSS (DE-588)4056588-9 s DE-604 Gröttrup, Sören 1982- (DE-588)1037718038 aut (DE-604)BV048304299 2 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=033684029&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wendler, Tilo Gröttrup, Sören 1982- Data Mining with SPSS Modeler Theory, Exercises and Solutions SPSS (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4056588-9 (DE-588)4056995-0 (DE-588)4428654-5 |
| title | Data Mining with SPSS Modeler Theory, Exercises and Solutions |
| title_auth | Data Mining with SPSS Modeler Theory, Exercises and Solutions |
| title_exact_search | Data Mining with SPSS Modeler Theory, Exercises and Solutions |
| title_exact_search_txtP | Data Mining with SPSS Modeler Theory, Exercises and Solutions |
| title_full | Data Mining with SPSS Modeler Theory, Exercises and Solutions Volume 2 Volume 2 Tilo Wendler ; Sören Gröttrup |
| title_fullStr | Data Mining with SPSS Modeler Theory, Exercises and Solutions Volume 2 Volume 2 Tilo Wendler ; Sören Gröttrup |
| title_full_unstemmed | Data Mining with SPSS Modeler Theory, Exercises and Solutions Volume 2 Volume 2 Tilo Wendler ; Sören Gröttrup |
| title_short | Data Mining with SPSS Modeler |
| title_sort | data mining with spss modeler theory exercises and solutions volume 2 |
| title_sub | Theory, Exercises and Solutions |
| topic | SPSS (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | SPSS Statistik Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033684029&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV048304299 |
| work_keys_str_mv | AT wendlertilo dataminingwithspssmodelertheoryexercisesandsolutionsvolume2 AT grottrupsoren dataminingwithspssmodelertheoryexercisesandsolutionsvolume2 |