Missing Values in Oracle Data Mining

NULL values usually indicate missing values. The following ODM algorithms assume that NULL values are indicators of missing values (and are not indicators of sparse data): Naive Bayes, Adaptive Bayes Networks, Attribute Importance, k-Means (Java interface), and O-Cluster.

Most ODM algorithms are robust in handling missing values and do not require users to treat missing values in any special way. ODM will ignore missing values but will use non-missing data in a case. See Recommended Missing Values Treatments for the exceptions.

Recommended Missing Values Treatments

There are several ways to treat missing values; one way is to replace the missing value with a "typical" value, such as the mean or the mode.

For the following algorithms, for best results, replace missing values with the mean for numerical data and the mode for categorical data:

If you invoke the Missing Values Transformation from a Mining Activity, default treatments are specified.