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Cluster detection: one type of pattern recognition that is particularly useful is recognizing distinct clusters or sub-categories within the data. Without data mining, an analyst would have to look at the data and decide on a set of categories which they believe captures the relevant distinctions between apparent groups in the data. This would risk missing important categories. With data mining it is possible to let the data itself determine the groups. This is one of the black-box type of algorithms that are hard to understand. But in a simple example - again with purchasing behavior - we can imagine that the purchasing habits of different hobbyists would look quite different from each other: gardeners, fishermen and model airplane enthusiasts would all be quite distinct. Machine learning algorithms can detect all of the different subgroups within a dataset that differ significantly from each other.

Classification: If an existing structure is already known, data mining can be used to classify new cases into these pre-determined categories. Learning from a large set of pre-classified examples, algorithms can detect persistent systemic differences between items in each group and apply these rules to new classification problems. Spam filters are a great example of this - large sets of emails that have been identified as spam have enabled filters to notice differences in word usage between legitimate and spam messages, and classify incoming messages according to these rules with a high degree of accuracy.

Regression: Data mining can be used to construct predictive models based on many variables. Facebook, for example, might be interested in predicting future engagement for a user based on past behavior. Factors like the amount of personal information shared, number of photos tagged, friend requests initiated or accepted, comments, likes etc. could all be included in such a model. Over time, this model could be honed to include or weight things differently as Facebook compares how the predictions differ from observed behavior. Ultimately these findings could be used to guide design in order to encourage more of the behaviors that seem to lead to increased engagement over time.

The patterns detected and structures revealed by the descriptive data mining are then often applied to predict other aspects of the data. Amazon offers a useful example of how descriptive findings are used for prediction. The (hypothetical) association between cocktail shaker and martini glass purchases, for instance, could be used, along with many other similar associations, as part of a model predicting the likelihood that a particular user will make a particular purchase. This model could match all such associations with a user's purchasing history, and predict which products they are most likely to purchase. Amazon can then serve ads based on what that user is most likely to buy.

Data mining, in this way, can grant immense inferential power. If an algorithm can correctly classify a case into known category based on limited data, it is possible to estimate a wide-range of other information about that case based on the properties of all the other cases in that category. This may sound dry, but it is how most successful Internet companies make their money and from where they draw their power.

Image: Reuters.