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Using Java Data Mining to Develop Advanced Analytics Applications

The predictive capabilities of enterprise Java apps

With the standardization of the Java Data Mining (JDM) API, Enterprise Java applications have been given predictive technologies.

Data mining is a widely accepted technology used for extracting hidden patterns from data. It is used to solve many business problems like identifying cross-sell or up-sell opportunities for specific customers based on customer profiles and purchase patterns, predicting which customers are likely to churn, creating effective product campaigns, detecting fraud, and finding natural segments.

More and more data mining algorithms are being embedded in databases. Advanced analytics, like data mining, is now widely integrated with applications. The objective of this article is to introduce Java developers to data mining and explain how the JDM standard can be used to integrate this technology with enterprise applications.

Data Mining Functions
Data mining offers different techniques, aka mining functions, that can be used depending on the type of problem to be solved. For example, a marketing manager who wants to find out which customers are likely to buy a new product can use the classification function. Similarly a supermarket manager who wants to determine which products to put next to milk and eggs, or what coupons to issue to a given customer to promote the purchase of related items can use the association function.

Data mining functions are divided into two main types called supervised (directed) and unsupervised (undirected).

Supervised functions are used to predict a value. They require a user to specify a set of predictor attributes and a target attribute. Predictors are the attributes used to predict the target attribute value. For example, a customer's age, address, occupation, and products purchased can be used to predict the target attribute "Will the customer buy the new product? (YES/NO)."

Classification and regression are categorized as supervised functions. Classification is used to predict discrete values, e.g., "buy" or "notBuy," and regression is used to predict continuous values, e.g., salary or price.

Unsupervised functions are used to find the intrinsic structure, relations, or affinities in data. Unsupervised mining doesn't use a target. Clustering and association functions come under this category. Clustering is used to find the natural groupings of data, and association is used to infer co-occurance rules from the data.

The Data Mining Process
Typically data mining projects are initiated by a business problem. For example, a CEO could ask, "How can I target the right customers to maximize profits?" Once the business problem is defined, the next step is to understand the data available and select the appropriate data to solve the problem. Based on the data's characteristics, prepare the data for mining. Select the right mining function and build a mining model with the data. After building the model, evaluate the model results. After evaluation, deploy the model. The CRISP-DM standard details the typical data mining process. Figure 1 illustrates a typical data mining process.

Enterprise applications like CRM analytics try to automate the data-mining process for common problems like intelligent marketing campaigns and market-basket analysis.

JDM API Overview
The Java Community Process (JCP) released the JDM 1.0 standard in August of 2004. JDM provides an industry standard API to integrate data mining functionality with applications. It facilitates the development of vendor-neutral data mining tools/solutions. It supports many commonly used mining functions and algorithms.

JDM uses the factory-method pattern to define Java interfaces that can be implemented in a vendor-neutral fashion. In the analytics business there's a broad set of data mining vendors who sell everything from a complete data mining solution to a single mining function. JDM conformance states that even a vendor with one algorithm/function can be JDM-conformant.

In JDM, javax.datamining is the base package that defines infrastructure interfaces and exception classes. Sub-packages are divided by mining function type, algorithm type, and core sub-packages. Core subpackages are javax.datamining.resource, javax.datamining.base, javax.datamining.data. The resource package defines connection-related interfaces that enable the applications to access Data Mining Engine (DME). The base package defines prime objects like mining model. The data package defines all physical and logical data-related interfaces. The javax.datamining.supervised package defines the supervised function-related interfaces and the javax.datamining.algorithm package contains all mining algorithm subclass packages.

Solving the Customer Churn Problem Using JDM
Problem Definition
Customer attrition is one of the big problems companies face. Knowing which customers are likely to leave can be used to develop a customer-retention strategy. Using data-mining classifications one can predict which customers are likely to leave. In the telecommunications industry, this problem is known as customer churn. Churn is a measure of the number of customers who leave or switch to competitors.

More Stories By Sunil Venkayala

Sunil Venkayala is a J2EE and XML group leader and principal member of technical staff at Oracle Data Mining Technologies group. He is an expert group member of the Java Data Mining (JDM) standard developed under JSR-73. Sunil has more than five years of experience in developing applications using predictive technologies available in the Oracle Database. He has more than seven years of experience working with Java and Internet technologies.

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