Add Hydrogen – PaDEL, 313
Add Noise, 375
Aggregate, 295, 371
Aggregation, 84
All-knn-selection, 368
ANOVA, 294, 435
Append, 219, 263, 295, 375, 404
Apply Model, 38, 89, 131, 202, 222, 265, 274, 291, 316, 415
Apply Threshold, 90
approximate Local Correlation Integral, 408
Attribute Based Item Recommendation, 136
Attributes by Weight, 323
Backward Elimination, 323
Bootstrap Validation, 326
Border / Interior Classification, 353
Calculate Descriptors – PaDEL, 313
Class assigner, 381
Class Iterator, 386
Clear Log, 375
Cluster Distance Performance, 416
Cluster Internal Evaluation, 182
Cluster-Based Local Outlier Factor (CBLOF), 409
Clustering (k-means(fast)), 242
Color Component Extractor, 337
Color to gray-scale, 337, 352
Combine Documents, 219
Compare ROCs, 93
Connectivity-Based Outlier Factor (COF), 407
Convert to 3D – PaDEL, 313
Count Substructure – PaDEL, 313
Create Association Rules, 114, 239
Create Lift Chart, 90
Create Threshold, 90
Cross Distance, 135
DBScan, 185
Decision Tree, 25, 92, 150, 301, 429
Declare Missing Values, 111
Detect Aromaticity – PaDEL, 313
Detect Dimensionality – PaDEL, 313
Detect Outlier (Densities), 397
Detect Outlier (Distance), 397
Detector, 357
Dichomatize, 321
Discretize, 145
Discretize by Binning, 53, 56, 87
Discretize by User Specification, 87
dLog Distance, 351
Documents to Data, 219
ENN selection, 368
ENN Weighting, 384
Execute Process, 164
Execute Script, 416, 438
Extract Cluster Prototypes, 390
Extract Content, 229
Extract Macro, 275, 325, 381, 419, 438
FCM, 381
Feature Selection Stability Validation, 268
Filter Examples, 53, 56, 83, 111, 147, 276, 286, 323, 343, 412
Filter Stopwords, 201
Filter Stopwords (English), 237
Filter Tokens (by Length), 237
Forward Selection, 323
FP-Growth, 113, 239
Free Memory, 265, 325
Fuzzy C-Means, 381
Gaussian Blur, 335, 358
Generate Attributes, 82, 217, 269, 275, 295, 322, 412
Generate Data, 162, 403
Generate ID, 326
Generate Macro, 381
Generate n-Grams (Characters), 224
Generate N-Grams (Terms), 200, 240
Generate Weight (Stratification), 88
Get Page, 229
Global Feature Extractor from a Single
Image, 345, 351
Global statistics, 345, 352
Grayscale to color, 337
Grouped ANOVA, 303
Guess Type, 85
Handle Exception, 375, 435
Histogram, 352
Histogram Equalization, 336
IB3 Selection, 370
Image Combinator, 339
Impute Missing Values, 87
Influenced Outlierness (INFLO), 408
Interest Points Visualizer, 357
Inverse, 343
Item Attribute k-NN, 137
Item k-NN, 131, 137
Iterative Component Analysis, 47
Join, 82, 135, 323, 413
K-Means, 167, 185, 300, 417
K-medoids, 185
k-NN, 33, 42, 45, 371, 381, 431
k-NN Global Anomaly Score, 404
Keep Document Parts, 212
LDA, 375
Learning Vector Quantization, 381
LibSVM, 414
Linear Discriminant Analysis, 375
Linear Regression, 93, 317
Local Correlation Integral (LOCI), 408
Local Density Cluster-Based Outlier Factor
(LDCOF), 410
Local Outlier Factor (LOF), 406
Local Outlier Probability (LoOP), 408
Log, 89, 148, 265, 325, 371, 432
Log to Data, 138, 325, 371, 417, 433
Logistic Regression, 93, 323
Look Up table applier, 337
Loop, 148, 267, 336, 381, 431
Loop and Deliver Best, 323
Loop Attributes, 322
Loop Datasets, 375
Loop Examples, 417
Loop Files, 217, 336, 429
Loop Labels, 384
Loop Models, 387
Loop Parameters, 185, 263, 266, 375, 387
Loop Subset, 325
LVQ, 381
Macro, 336
Map, 381, 403, 415
Materialize Data, 167, 387
MC Selection, 367
Merge Segments to Image, 343
Model Combiner, 136
Modelling, 315
Multiple Color Image Opener, 345, 350
Multiple Grayscale Image Opener, 336
Multiply, 83, 135, 147, 271, 295, 323, 338,413
Naive Bayes, 53, 65, 150, 202, 222, 431
Neural Net, 290, 291
Nominal to Binominal, 321
Nominal to Binominal, 82, 111
Nominal to Numerical, 93
Nominal to Text, 219
Normalize, 185, 371, 399, 413
Numerical to Binominal, 37, 40, 237, 295
Numerical to Nominal, 381
Numerical to Polynominal, 37, 40
Open Color Image, 336
Open Grayscale Image, 335, 357
Optimize Parameters, 93, 187
Optimize Parameters (Grid), 415
Optimize Search, 325
Optimize Selection, 150, 325
Order-based Block Color, 352
PaDEL, 312
Parse Numbers, 138, 378
Performance, 38, 51, 89, 131, 137, 202, 265, 292, 429
Performance (Binominal Classification), 53, 89
Performance (Classification), 38, 48, 148,415
Performance – Regression, 317
Performance Binominal Classification, 38
Permutation, 326
Pivot, 433
poi generator, 346
Principal Component Analysis, 47
Process Documents, 135, 197
Process Documents from Data, 135, 197,221
Process Documents from Files, 235
Provide Macro as Log Value, 138, 381,419, 432
Random Forest, 150, 273, 323, 431
Random Selection, 367, 384
Read AML, 124, 145, 183, 262, 428
Read Compounds – PaDEL, 313
Read CSV, 36, 46, 105, 166, 197, 217, 411
Read Database, 82, 105
Read Excel, 23, 36, 67
Read Image Set, 356
Read URL, 36
Recall, 375, 416
Remap Binominals, 270, 321
Remember, 167, 375, 416
Remove Correlated Attributes, 47
Remove Salt – PaDEL, 313
Remove Unused Values, 286
Remove Useless Attributes, 47, 87, 323
Rename, 36, 40, 166, 217, 262, 295, 321
Rename by Replacing, 53, 56
Rename Compound – PaDEL, 313
RENN selection, 375
Replace, 112
Replace Missing Values, 86, 111, 185, 299
Replace Tokens, 224
Replenish Values, 322
Retrieve, 82, 197, 220, 321
RMHC selection, 367
RNG selection, 370
ROI Statistics, 358
ROI statistics, 343
Sample, 88, 109, 200, 271
Sample (Bootstrapping), 271
Sample (Stratified), 387
Segment Feature Extraction, 343
Segment Feature Extractor, 358
Segment Filter by Example Set, 343
Segment Mask Processor, 358
Select, 434
Select Attributes, 23, 47, 67, 82, 109, 145,167, 183, 217, 242, 286, 411
Select by MRMR/CFS, 265
Select by Weights, 323
Select Subprocess, 148, 185, 378, 431
Set Minus, 135
Set Role, 36, 37, 86, 130, 145, 217, 219,326, 412
Simple Validation, 148
Singular Value Decomposition, 47
Sort, 322
Split Data, 48, 219
Split Validation, 37
Statistical region merging, 343, 358
Stem (Porter), 200, 237
Store, 197, 219, 322, 343, 404
Subprocess, 21, 145, 275, 343, 432
Support Vector Clustering, 185
Support Vector Machine, 431
Support Vector Machine (LibSVM), 403,415
SVD, 137
SVM, 342, 387
T-Test, 434
Text to Nominal, 237
Thresholding, 358
Tokenize, 197, 220, 237
Trainable segmentation, 342
Transform Cases, 200, 226, 237
Transpose, 323
Unescape HTML, 229
Update Model, 131
Validation, 221
Vector Quantization, 381
Viola-Jones, 357
VQ, 381
W-LMT, 150
W-Random Forest, 265
W-SimpleCart, 150
Weight by Correlation, 323
Weight by Information Gain, 323
Weight by Tree Importance, 323
Weight Transformation, 386
Wrapper Split Validation, 150
Wrapper X-Validation, 265
Write AML, 276
Write Compounds – PaDEL, 313
Write Image to File, 335, 336
Write Model, 94
Write Weights, 151, 263
X-Means, 409
X-Validation, 38, 53, 57, 87, 203, 222, 263, 290, 429