Data Mining - Professional SQL Server Analysis Services 2005 with MDX - page 728

Enclosed below are brief descriptions of the nine data mining algorithms
supported in Analysis Services 2005. The description will give you an
overview of the algorithm and scenarios where the algorithm can be util-
ized. We recommend you to refer to SQL Server Analysis Services doc-
umentation for details such as algorithm properties, their values, and
various content types supported by the algorithm for input and predict-
able columns. Following these descriptions you will learn two data min-
ing algorithms in detail by creating mining models using the data mining
wizards.
Microsoft Decision Trees
Microsoft Decision Trees is a classification algorithm that is used for pre-
dictive modeling and analysis. A classification algorithm is an algorithm
that selects the best possible outcome for an input data from a set of
possible outcomes. An input data set called the training data that con-
tains several attributes is provided as input to the algorithm. Usage of
the attributes as either input or predictable are also provided to the al-
gorithm. The classification algorithm analyzes the attributes of the input
data and arrives at a distribution, which includes a combination of in-
put attributes and their values that result in the value of the predictable
column. Microsoft Decision Trees is helpful in predicting both discrete
and continuous attributes. If the data type of the predictable attribute is
continuous, the algorithm is called Microsoft Regression Trees and there
are additional properties to control the behavior of the regression ana-
lysis.
Nave Bayes
Nave Bayes is another classification algorithm available in Analysis Services 2005
that is used for predictive analysis. The Nave Bayes algorithm calculates the value
of the predictable attribute based on the probabilities of the input attribute in the
training data set. Nave Bayes helps you to predict the outcome of the predictable
attribute quickly because it assumes the input attribute is independent. Compared
to the data mining algorithms in Analysis Services 2005, Nave Bayes is computa-
tionally less intense for model creation.