Scientific Understanding, Comprehensive Evolution and More Informed Evaluation of Various Sequential Pattern Mining Algorithms

As database resources get complex and bulky it not only becomes difficult to access specific information but also to extract relevant information from them. A way to address this issue is through sequential pattern mining technique. Sequential pattern mining is new trend in the domain of data mining and has many useful and exciting applications. In the sequential pattern mining approach, we mainly deal with attempting to discover a pattern that is sequential in nature. This helps us to predicting next event after a sequence or sequence-of-event(s). The success of such techniques lies in the design of their algorithm. Today, there are several competitive and efficient algorithms that cope with the popular and computationally expensive task of sequential pattern mining. Actually, these algorithms are more or less described on their own. This paper mainly focuses on the need, merits and demerits of different sequential rule mining algorithms and categorizing them according to their mining method, search method adopted, database formatting employed and other constraints as applied to the database. The basic inspiration to undertake this study is to provide a single platform-of-information that will serve as a ready reference for both the researchers and practitioners interested in the designing and implementation of sequential pattern mining algorithms depending upon categorized databases.