Oracle Application Server 10g Essentials

Data Mining

Data mining, as we define it in this chapter, is the use of mathematical algorithms to model relationships in the data that solve difficult problems involving large numbers of variables with unknown relationships. You might need such techniques if you are trying to solve such business problems as fraud detection, customer churn analysis, and marketing contact response prediction. The algorithms used to solve such problems include clustering techniques that show how business outcomes can fall within certain groups (such as for market basket analysis) and logic models (if A occurs, then B or C are possible outcomes), validated against small sample sets and then applied to larger data sets for prediction.

Since the Oracle9i database release, Oracle has provided a set of data mining algorithms in the database’s Data Mining Option for solving such problems. Algorithms now embedded in the database include:

• Naïve Bayes Associations

• Adaptive Bayes Networks

• Clustering, Support Vector Machines (SVM)

• Nonnegative Matrix Factorization (NMF)

These algorithms are accessible via a Java API.

Data mining applications can be custom-built using Oracle’s JDeveloper in combination with the Data Mining for Java interface. DM4J is used to develop, test, and score the models. It provides the ability to define metadata, tune the generated Java code, view generated XML files, and test application components. The J2EE-generated applications can be deployed within the database, to Oracle Application ...