Data structure and data size Size on its own is not enough to allow the effective use of data mining techniques. Suppose that a bank has huge amounts of data on just half a dozen business customers. There may be problems in processing and summarising the data, because of its size. Before one can think about dividing data between training sets and test sets, data from a large number of business customers would be needed. Approaches to data mining Data mining can perform a number of tasks, some of which are described below.
Classification finds a rule or a formula for organising data into classes.
For example, a bank may wish to classify clients requesting loans into categories based on the likelihood of repayment. A rule or formula for making the classification is developed from the data in the training set. The reliability of the rule or formula is then evaluated using the test set of data. This gives an indication of how well the procedure will work on the remaining bulk of the data. Like classification, clustering breaks a large database into different subgroups or clusters.