SEQUENTIAL PATTERN MINING ALGORITHMS – RECENT TRENDS

Abstract

: Sequential pattern mining is a technique of data mining whose objective is to identify statistically relevant patterns within a database with time-related data. It has a wide range of applications in variety of domains like education, healthcare, bioinformatics, web usage mining, telecommunications, intrusion detection etc. At present, most of the real sequence databases are incremental in nature. So there is a need to explore incremental and distributed pattern mining algorithms. Periodic pattern mining is a technique to discover periodic pattern which may be a pattern that repeats itself after a specific time interval. It has a wide range of applications in weather prediction, stock market analysis, web usage recommendation etc. Moreover, uncertain frequent pattern mining has become a popular research domain among researchers, as many real-life databases at present consist of uncertain and incomplete data. In this paper, a novel attempt is made to incorporate a systematic literature review of state-of-the-art techniques of sequential pattern mining which ranges from incremental pattern mining, periodic pattern mining and uncertain frequent pattern mining. Researchers in the field of pattern mining will find it very useful to get the information about various algorithms of different types of pattern mining.