Predicting Credit Risk One challenge data miners face is choosing which technique to apply to an analysis task. In many situations, analysts can use a variety of techniques, but each technique conducts the analysis and represents the results differently. This sidebar describes sample data for a credit risk classification task that each data mining technique can address. Association and Sequencing Selling as many different products as possible to your customers maximizes their value to your business. Association and sequencing techniques, also known as market basket analysis, can reveal purchasing patterns such as which products are purchased at the same time or which products are purchased after other products.
Pattern detection has many other applications in finance and other business processes.
Classification and Regression Effective data mining can deliver a high payoff for your business. Classification and regression can predict customer behavior, signal potentially fraudulent transactions, and forecast store profitability, to name just a few of many applications. Na ve-Bayes, and nearest neighbor. When a businessperson needs to make a decision based on several factors, a decision tree can help identify which factors to consider and how each factor has historically been associated with different outcomes of the decision. Data mining tools that produce decision trees create graphical models and text rules that can both predict behavior and explain patterns.