Abstract
This chapter illustrates various Java Data Mining (JDM) concepts related to data specifications, classification, regression, attribute importance, association rules, and clustering functions. The chapter also reveals that data specifications are divided into physical and logical specifications to facilitate reusability. Model build settings provide function-specific settings and algorithm settings that are used to tune the models for problem-specific requirements. JDM provides test metrics for supervised models to understand model quality. JDM supports the model apply for supervised and clustering models, providing control over the output values.JDM defines algorithm settings for decision trees, the support vector machine, naïve bayes, feed forward neural networks, and k-means algorithm settings. As JDM defines algorithm selection as an optional step, most data mining tools provide a default or preselected algorithm for each mining function. Some data mining tools automate finding the most appropriate algorithm and its settings based on the data and user-specified problem characteristics. If the data miner does not specify the algorithm to be used, the JDM implementation chooses the algorithm.
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