Data Science

by John D. Kelleher

Using data science, we can extract different types of patterns. For example, we might want to extract patterns that help us to identify groups of customers exhibiting similar behavior and tastes. In business jargon, this task is known as customer segmentation, and in data science terminology it is called clustering. Alternatively, we might want to extract a pattern that identifies products that are frequently bought together, a process called association-rule mining. Or we might want to extract patterns that identify strange or abnormal events, such as fraudulent insurance claims, a process known as anomaly or outlier detection. Finally, we might want to identify patterns that help us to classify things. For example, the following rule illustrates what a classification pattern extracted from an email data set might look like: If an email contains the phrase “Make money easily,” it is likely to be a spam email. Identifying these types of classification rules is known as prediction. The word prediction might seem an odd choice because the rule doesn’t predict what will happen in the future: the email already is or isn’t a spam email. So it is best to think of prediction patterns as predicting the missing value of an attribute rather than as predicting the future. In this example, we are predicting whether the email classification attribute should have the value “spam” or not. Although we can use data science to extract different types of patterns, we always want the patterns...