KRIBII Rajae, FAKIR Youssef, Mining Frequent Sequential Patterns, Journal of Big Data Research, Volume 1, Issue 2, 2021, Pages 20-37, ISSN 2768-0207, https://doi.org/10.14302/issn.2768-0207.jbr-21-3455.
(https://oapgroup.org/jbr/article/1597)
Abstract: In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequential algorithm (GSP) and Sequential PAttern Discovery using Equivalence classes (SPADE). These two algorithms are based on the Apriori algorithms. Experimental results have shown that SPADE consumes less time than GSP algorithm.
Keywords: Data mining; Sequential Patterns; Apriori; SPADE; GSP.