A COMPARATIVE STUDY OF SEQUENTIAL PATTERN MINING ALGORITHMS

Abstract The concept of sequence Data Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikanth in the year 1995. Sequential pattern mining are used in broad applications. It is used to uncover all sequential pattern which satisfy the user specified constraint, from the given sequence database. However, it is also a difficult to generate and examine a number of intermediate sub-sequences. Sequential pattern mining is the method of finding interesting sequential patterns among the large database. The application can be used in various domain like natural disaster, sales record analysis, marketing strategy, shopping sequences, medical treatment and DNA sequences etc. This paper presents the traditional sequential pattern mining algorithms. The sequential pattern mining algorithms are classified into four classes: First, on the basis of Apriori-based algorithm, Second on Breadth First search-strategy, third on Depth First search-strategy and fourth, on Sequential closedpattern algorithm. The surveys of all these algorithms are made on the basis on of various research perspectives. First algorithms are categorized using various approaches to solve the mining problem and then algorithms are compared each one with another by their various provided features and performance point of view.