Data Mining, 4th Edition

in finding nearest-neighbor, 137–138, 137f

for training instances, 137f

updating, 138–139

Keras, 465–466

Kernel density estimation, 361–362

Kernel logistic regression, 261

Kernel perceptron, 260–261

Kernel regression, 403

Kernel ridge regression, 258–259

computational expense, 259

computational simplicity, 259

drawback, 259

Kernels, conditional probability models using, 402–403

Kernel trick, 258

K-means algorithm, 355

K-means clustering, 142–143

iterations, 144

k-means++, 144

seeds, 144

K-nearest-neighbor method, 85

Knowledge, 37

background, 508

metadata, 513

prior domain, 513

Knowledge Flow interface, 554, 555, 564–567See also Weka workbench

Associations panel, 566

Classifiers folder, 566

Clusters folder, 566

components, 566 ...