Three typical types of Data Mining applications:

    Classification

    Regression

    Clustering

Classification

In a classification type problem, we have a variable of interest which is categorical in nature. For example, this could be:

    Classification of credit risk, either good or bad

    Classifying patients as high risk for heart disease

    Classifying individuals as high risk for fraudulent behavior

The goals of the classification problem can include:

    Finding variables that are strongly related to the variable of interest

    Developing a predictive model where a set of variables are used to

    Classify the variable of interest

Regression

In a regression type problem, we have a variable of interest which is continuous in nature. For example, this could be:

    A measurement for a manufacturing process

    Revenue in dollars

    Decrease in cholesterol after taking medication

The goals of the regression problem are similar to classification and can include:

    Finding variables that are strongly related to the variable of interest

    Developing a predictive model where a set of varicbles are used to

　 predict the variable of interest

Clustering

In a clustering type problem, there is not a traditional variable of interest. Instead, the data needs sorted into cluster. For example:

　　Clustering indibiduals for a marketing campaign

　　Clustering symptoms in medical research to find relationships

　　Finding clusters of bands, based on customer responses

The goals of cluster analysis problem can include:

　　Finding variables that are most highly influence cluster assignment

　　Comparing the clusters across variables of interest

　　Assigning new cases to clusters and measuring the strength of cluster membership