A Review of Modern Sequential Rule Mining Techniques

Abstract

Sequential rule mining is an important data mining task with wide applications. The current algorithms for discovering sequential rules common to several sequences use very restrictive definitions of sequential rules. Among various data mining objectives the mining of frequent patterns has been the focus of knowledge discovery in databases. In this paper, we aim to investigate efficient algorithm for mining including association rule and sequential patterns. The time and space consumption of proposed algorithm will be lesser in comparison to previous algorithm. From the broad variety of efficient algorithm that has been developed we will compare the most important ones. We will analyze the performance of various algorithms on the basis of both their run time performance and theoretical considerations. We use pattern growth approach for discovering valid rules such that it can be much more efficient and scalable. General Terms Data Mining, Sequential Rule Mining, Market Basket Problem