Markov Models in the Analysis of Frequent Patterns in Financial Data

Frequent sequence mining is one of the main challenges in data mining and especially in large databases, which consist of millions of records. There is a number of different applications where frequent sequence mining is very important: medicine, finance, internet behavioural data, marketing data, etc. Exact frequent sequence mining methods make multiple passes over the database and if the database is large, then it is a time consuming and expensive task. Approximate methods for frequent sequence mining are faster than exact methods because instead of doing multiple passes over the original database, they analyze a much shorter sample of the original database formed in a specific way. This paper presents Markov Property Based Method (MPBM) – an approximate method for mining frequent sequences based on kth order Markov models, which makes only several passes over the original database. The method has been implemented and evaluated using real-world foreign exchange database and compared to exact and approximate frequent sequent mining algorithms.