Data Mining - Professional SQL Server Analysis Services 2005 with MDX - page 729

Microsoft Clustering
The Microsoft Clustering algorithm is a segmentation algorithm that
helps in grouping the sample data set into segments based on the char-
acteristics. The clustering algorithm helps in identifying relationships ex-
isting within a specific data set. A typical example would be grouping
store customers based on their characteristics of sales patterns. Based
on this information you can classify the importance of certain custom-
ers to your bottom-line. The Microsoft Clustering algorithm is unique be-
cause it is a scalable algorithm that is not constrained by the size of
the data set. Unlike the Decision Trees or Nave Bayes algorithm, the
Microsoft Clustering algorithm does not require you to specify a predict-
able attribute for building the model.
Sequence Clustering
As the name indicates, the Sequence Clustering algorithm helps in
grouping sequences in the sample data. Similar to the clustering al-
gorithm, the sequence clustering algorithm groups the data sets but
based on the sequences instead of the attributes of the customers. An
example of where Sequence Clustering would be used is to group the
customers based on the navigation paths of the Web site they have vis-
ited. Based on the sequence, the customer can be prompted to go to a
Web page that would be of interest.
Association Rules
The Microsoft Association algorithm is an algorithm that typically helps
identify associations or relationships between products that are pur-
chased. If you have shopped at http://www.Amazon.com you have
likely noticed information "people who have purchased item one have
also purchased item two." Identifying the association between products
purchased is called market-basket analysis. The algorithm helps in ana-
lyzing products in a customer's shopping basket, and predicts other
products the customer is likely to buy. That prediction is based on pur-
chase co-occurrence of similar products by other customers. This al-