Data Mining Functions:
Data mining functions can be classified as supervised and
unsupervised. A supervised mining function predicts a value
based on a pre set target. The target needs to be defined and it
will determine how well it is matching the target values.
Unsupervised functions do not need a target and are used to
identify structures and, relations in data. Another
classification is based on how is the data mining is done. They
can be descriptive or predictive. Descriptive data mining creates a
dataset that is concise and presents general properties about
the data. The predictive data mining performs inferences on the
available set of data and tries to predict the outcome for new
data sets.
The above categories are for the classification of the functions
provided by JDM. The actual data mining functions are described
below.
Classification is a type of supervised function where an
algorithm builds a model based on a set of predefined predictors
used to predict the target. It is usually used in business
modeling and credit analysis.
The second type of function is Regression, which is a type of
supervised function. Regression is usually used in financial
forecasting and drug response modeling.
The third type is Attribute importance, which can be both a
supervised and unsupervised function. Attribute importance
identifies which attributes are important for building a model.
This improves the time to build a model and the accuracy of the
model by eliminating noise attributes from the model. The
function classifies all the attributes by their relevant
importance in building the model and allows the user to choose
the most important attributes that are needed to build the
model.
The fourth type is Clustering. Clustering identifies clusters in
the data. A cluster is a collection of objects that are similar
to each other. Clustering is used primarily in customer
segmentation, product groupings, and text mining.
The fifth type of function is Association. Association looks for
patterns of relationships in data. It looks for relationships
between a set of data and looks for patterns of this kind
throughout. This is useful in analyzing consumer
behavior.
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