Data Mining - Professional SQL Server Analysis Services 2005 with MDX - page 726

Data Mining Algorithms in Analysis Services 2005
Analysis Services 2005 provides you with nine data mining algorithms
that you can utilize to solve various business problems. These algorithms
can be broadly classified into five categories based on the nature of the
business problem they can be applied to. They are
▪ Classification
▪ Regression
▪ Segmentation
▪ Sequence analysis
▪ Association
Classification data mining algorithms help solve business problems such as identify-
ing the type of membership (Platinum, Gold, Silver, Bronze) a new customer should
receive or whether the requested loan can be approved for a customer based on
his or her attributes. Classification algorithms predict one or more discrete variables
based on the attributes of the input data. Discrete variables are variables which con-
tain a limited set of values. Some examples of discrete variables are Gender, Num-
ber of children in a house, and number of cars owned by a customer.
Regression algorithms are similar to classification algorithms; instead of predicting
discrete attributes, however, they predict one or more continuous variables.
Continuous variables are variables that can have many values. Examples of con-
tinuous variables are yearly income, age of a person, and commute distance to
work. The algorithms belonging to the regression category should be provided with
at least one input attribute that is of type continuous. For example, assume you
want to predict the sale price of your house, a continuous value, and determine the
profit you would make by selling the house. The price of the house would depend
on several factors, such as square feet area (another continuous value), zip code,
and house type (single family, condo, or town home), which are discrete variables.
Hence regression algorithms are primarily suited for business problems where you
have at least one continuous attribute as input and one or more attributes as pre-
dictable attributes.