Data Mining for Maximal Frequent Patterns in Sequence Groups

Abstract

In this paper, we give a general treatment for mining some kinds of sequences such as customer sequences, document sequences, and DNA sequences. Large collections of transaction, document, and genomic information have been accumulated in recent years, and embedded latently in it there is potentially significant knowledge for exploitation in the retailing industry, in information retrieval, and in medicine and the pharmaceutical industry, respectively. The approach taken here to the distillation of such knowledge is to detect strings in sequences which appear frequently, either within a given sequence (e.g. for a particular customer, document, or patient) or across sequences (e.g. from different customers, documents, or patients sharing a particular transaction, information retrieval, or medical diagnosis; respectively). Patterns are strings that occur very frequently. These concepts are generalisation of the concept of motifs for DNA sequences. There are interesting differences between the 3 applications.