mining function A major subdomain of data mining that shares
common high-level characteristics. For JDM 1.1, functions include:
classification, regression, attribute importance, association, and
mining object repository (MOR) The logical or physical architec-
tural component that stores JDM mining objects, such as tasks,
models, settings, and their components.
mining result The end product(s) of a mining operation. For exam-
ple, a build task produces a mining model, a test task produces a test
missing value A data value for an attribute of a case that is missing
because it was not measured, not answered, was unknown or was
lost. Data mining methods vary in the way they treat missing values.
Typically, they may ignore the missing values, omit any records
containing missing values, replace missing values with the mode or
mean, or infer missing values from existing values.
missing values treatment A transformation that specifies how to
replace missing values, for example, with the attribute mean or
mode, a specific value, and so on.
model A compact representation of patterns found using historical
data. A model is the result of executing a build task . Model represen-
tation is specific to the algorithm used. A model can be descriptive or
predictive. A descriptive model helps in understanding underlying
processes or behavior. A predictive model is an equation or set of
rules that makes it possible to predict an unseen or unmeasured
value (the dependent attribute or target) from other, known values
(independent attributes or predictors).
model comparison A phase in the data mining process that
involves comparing multiple models to select the model of highest
quality or that best matches the needs of the business problem. Com-
parison can be based on various criteria, for example, maximum
accuracy, minimum Type I error, and so on.
model detail The specific representation of a model that is algo-
rithm dependent. For example, a decision tree has specific model
detail of the tree nodes and their relationships.
model signature A collection of signature attributes, derived from
the logical data used to build a model. The input data to a model for
scoring must be compatible with the model signature.
Meta Object Facility.
See mining object repository .