??? Clustering techniques that show how business outcomes can fall into certain
groups, such as insurance claims versus time for various age brackets. In this
example, once a low-risk group is found or classified, further research into influencing
factors or ???associations??? might take place.
??? Logic models (if A occurs, then B or C are possible outcomes) validated against
small sample sets and then applied to larger data models for prediction, commonly
known as decision trees.
??? Neural networks ???trained??? against small sets, with known results to be applied
later against a much larger set.
??? Anomaly detection used to detect outliers and rare events.
??? Visualization techniques used to graphically plot variables and understand
which variables are key to a particular outcome.
Data mining is often used to solve difficult business problems such as fraud detection
and churn in micro-opportunity marketing, as well as in other areas where many
variables can influence an outcome. Companies servicing credit cards use data mining
to track unusual usage??”for example, the unexpected charging to a credit card of
expensive jewelry in a city not normally traveled to by the cardholder.
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