Articles : The Java Data Mining API :

Data Mining Functions:

The above categories are for the classification of the functions provided by JDM. The actual data mining functions are described below.

Classification is a type of supervised function where an algorithm builds a model based on a set of predefined predictors used to predict the target. It is usually used in business modeling and credit analysis.

The second type of function is Regression, which is a type of supervised function. Regression is usually used in financial forecasting and drug response modeling.

The third type is Attribute importance, which can be both a supervised and unsupervised function. Attribute importance identifies which attributes are important for building a model. This improves the time to build a model and the accuracy of the model by eliminating noise attributes from the model. The function classifies all the attributes by their relevant importance in building the model and allows the user to choose the most important attributes that are needed to build the model.

The fourth type is Clustering. Clustering identifies clusters in the data. A cluster is a collection of objects that are similar to each other. Clustering is used primarily in customer segmentation, product groupings, and text mining.

The fifth type of function is Association. Association looks for patterns of relationships in data. It looks for relationships between a set of data and looks for patterns of this kind throughout. This is useful in analyzing consumer behavior.

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