What would you recommend?

You customer wants to segment their customers1 demographic data into those that use and do
not use loyalty card. What would you recommend?

A.
Use Oracle OLAP Option.

B.
Use Oracle SQL Analytic Functions.

C.
Use classification algorithm in Oracle Data Mining.

D.
Use non-negative matrix factorization in Oracle Data Mining.

Explanation:

Classification is a data mining function that assigns items in a collection to target
categories or classes. The goal of classification is to accurately predict the target class
for each case in the data. For example, a classification model could be used to identify
loan applicants as low, medium, or high credit risks.
The simplest type of classification problem is binary classification. In binary
classification, the target attribute has only two possible values: for example, high credit
rating or low credit rating
Note:

Oracle Data Mining provides the following algorithms for classification:
* Decision Tree
Decision trees automatically generate rules, which are conditional statements that
reveal the logic used to build the tree.
* Naive Bayes
Naive Bayes uses Bayes Theorem, a formula that calculates a probability by
counting the frequency of values and combinations of values in the historical data.
* Generalized Linear Models (GLM)
GLM is a popular statistical technique for linear modeling. Oracle Data Mining
implements GLM for binary classification and for regression.
GLM provides extensive coefficient statistics and model statistics, as well as row
diagnostics. GLM also supports confidence bounds.
* Support Vector Machine
Support Vector Machine (SVM) is a powerful, state-of-the-art algorithm based on
linear and nonlinear regression. Oracle Data Mining implements SVM for binary
and multiclass classification.
Reference:
Reference: Oracle Data Mining, Concepts 11g Release 1 (11.1)
http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129.pdf