Glossary - Java Data Mining: Strategy, Standard, and Practice - page 469

attribute type Specifies how a logical attribute is to be interpreted
during model building. Commonly, four types of attributes are
distinguished: nominal or categorical attributes, ordinal or rank
attributes, interval attributes, and real or real-valued attributes (also
called true measures). JDM restricts itself to three types: categorical,
numerical , and ordinal .
attribute usage Specifies how a logical attribute is to be used when
building a model: active, supplementary, or inactive .
binning A data mining transformation which maps a set of input
values to a smaller set of bins. The input values may be discrete or
continuous.
build The data mining operation that produces a model.
build data The data used as input to building a model. Sometimes
referred to as the training data .
build settings A collection of settings, or parameters, specifying the
type of data mining model to build, including mining function and
algorithm settings. Build settings exist for each of the mining func-
tions, including: classification, regression, association, sequences,
attribute importance, and clustering.
build task A task that when executed builds a model as specified
by the build settings.
case A collection of related attribute values used as input to model
building, testing, or scoring. In a simple table, a case corresponds to
an individual record. In transactional format data, a case may be
represented by multiple records, where columns play the roles of
identifier, attribute name, and attribute value. See also single record
case and multi-record case .
case identifier The unique identifier associated with a case. Also
referred to as “case id.”
categorical attribute An attribute where the values correspond to
discrete categories. For example, state is a categorical attribute with
discrete values (CA, NY, MA, etc.). Categorical attributes are either
nonordered (nominal) like state, gender, and so on, or ordered (ordi-
nal) such as high, medium, or low temperatures.
Categorical attributes tell us which of several categories a thing
belongs to. For example, we can say that a beverage is BEER,
LIQUOR, SOFTDRINK, or WINE. Categorical attributes exhibit the
lowest degree of organization, since the set of categories such an
attribute may assume posses no systematic intrinsic organization or
order. The only relation between the categories of such attributes is