Association Rules and Sequential Patterns

Abstract

Association rules are an important class of regularities in data. Mining of association rules is a fundamental data mining task. It is perhaps the most important model invented and extensively studied by the database and data mining community. Its objective is to find all co-occurrence relationships, called associations, among data items. Since it was first introduced in 1993 by Agrawal et al. [9], it has attracted a great deal of attention. Many efficient algorithms, extensions and applications have been reported. The classic application of association rule mining is the market basket data analysis, which aims to discover how items purchased by customers in a supermarket (or a store) are associated. An example association rule is Cheese → Beer [support = 10%, confidence = 80%] The rule says that 10% customers buy Cheese and Beer together, and those who buy Cheese also buy Beer 80% of the time. Support and confi-dence are two measures of rule strength, which we will define later. This mining model is in fact very general and can be used in many ap-plications. For example, in the context of the Web and text documents, it can be used to find word co-occurrence relationships and Web usage pat-terns as we will see in later chapters. Association rule mining, however, does not consider the sequence in which the items are purchased. Sequential pattern mining takes care of that. An example of a sequential pattern is " 5% of customers buy bed first, then mattress and then pillows " The items are not purchased at the same time, but one after another. Such patterns are useful in Web usage mining for analyzing clickstreams from server logs. They are also useful for find-ing language or linguistic patterns from natural language texts.