The following glossary terms are provided from the Java Data
Mining specification [JSR-73 2005, JSR-247 2006].
accuracy In the context of a supervised model, accuracy refers to
how well the model can make predictions.
algorithm A specific technique or procedure for producing a data
mining model. An algorithm uses a specific model representation and
may support one or more functional areas. Examples include decision
trees, backpropagation neural networks, naïve bayes algorithms for
supervised mining functions, and apriori for the association mining
algorithm settings A collection of settings, or parameters, to affect
algorithm-specific behavior during model building.
anomaly detection A mining function that produces models for
detecting deviations from the norm in a dataset. The data provided
for model building consists of normal cases from which an anomaly
detection algorithm learns patterns that are captured in the resulting
model. Applying the model flags cases that deviate or are unusual
from the normal cases in some way.
antecedent In an association rule, the left-hand side is called the
antecedent. For example, in the rule “If A, then B,” “A” is the ante-
cedent. See also consequent .
Application Program Interface.