Home > Data Mining Algorithms > Anomaly Detection > Anomaly Detection Viewers a... > Association > AR Model Viewers and Algori... > Decision Tree > Expectation Maximization > EM Model Viewer and Algorit... > Generalized Linear Models > GLM Model Viewers and Algor... > k-Means > Naive Bayes > Nonnegative Matrix Factoriz... > NMF Model Viewer and Algori... > NMF Algorithm Settings
The Nonnegative Matrix Factorization (NMF) algorithm supports these settings:
Convergence Tolerance: Indicates the minimum convergence tolerance value. The default is 0.5.
Automatic preparation: ON (Default). Indicates automatic data preparation.
NMFS_NONNEGATIVE_SCORING: Enabled or Disabled. The default is Enabled (NMFS_NONNEG_SCORING_ENABLE).
Number of features: The default is to not specify the number of features. If you do not specify the number of features, then the algorithm determines the number of features.
To specify the number of features, then select Specify number of features and enter the integer number of features. The number of features must be a positive integer less than or equal to the minimum of the number of attributes and to the number of cases. In many cases, 5 or some other number less than or equal to 7 gives good results.
Number of iterations: Indicates the maximum number of iterations to be performed. The default is 50.
Random Seed: It is the random seed for the sample. The default value is -1.The seed can be changed. If you plan to repeat this operation to get the same results, ensure to use the same random seed.