Discovery of frequent patterns in large data collections

A method is given for the discovery of all frequent association rules a well known data mining problem and how the association rule algorithm can be extended to cover this problem is introduced.

Abstract

Data mining, or knowledge discovery in databases, aims at finding useful regularities in large data sets. Interest in the field is motivated by the growth of computerized data collections and by the high potential value of patterns discovered in those collections. For instance, bar code readers at supermarkets produce extensive amounts of data about purchases. An analysis of this data can reveal useful information about the shopping behavior of the customers. Association rules, for instance, are a class of patterns that tell which products tend to be purchased together. The general data mining task we consider is the following: given a class of patterns that possibly have occurrences in a given data collection, determine which patterns occur frequently and are thus probably the most useful ones. It is characteristic for data mining applications to deal with high volumes of both data and patterns. We address the algorithmic problems of determining efficiently which patterns are frequent...