Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data.
High speed makes it practical for users to analyze huge quantities of data.
Larger databases, in turn, yield improved predictions. In hands-on analyses, analysts must often limit the number of variables they examine because of time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full dimensionality of a database, without preselecting a subset of variables. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small segments of a population.