How ADP Transforms the Data

Table 4-1 shows how ADP prepares the data for each algorithm.

Table 4-1 Oracle Data Mining Algorithms With ADP

Algorithm	Mining Function	Treatment by ADP
Apriori	Association Rules	ADP has no effect on association rules.
Decision Tree	Classification	ADP has no effect on Decision Tree. Data preparation is handled by the algorithm.
Expectation Maximization	Clustering	Single-column (not nested) numerical columns that are modeled with Gaussian distributions are normalized with outlier-sensitive normalization. ADP has no effect on the other types of columns.
GLM	Classification and Regression	Numerical attributes are normalized with outlier-sensitive normalization.
k-Means	Clustering	Numerical attributes are normalized with outlier-sensitive normalization.
MDL	Attribute Importance	All attributes are binned with supervised binning.
Naive Bayes	Classification	All attributes are binned with supervised binning.
NMF	Feature Extraction	Numerical attributes are normalized with outlier-sensitive normalization.
O-Cluster	Clustering	Numerical attributes are binned with a specialized form of equi-width binning, which computes the number of bins per attribute automatically. Numerical columns with all nulls or a single value are removed.
SVD	Feature Extraction	Numerical attributes are normalized with outlier-sensitive normalization.
SVM	Classification, Anomaly Detection, and Regression	Numerical attributes are normalized with outlier-sensitive normalization.