
From the beginning of sequential pattern mining to the present, this field has received important attention within the data mining area, because it has a wide application in several significant computational problems. Many algorithms have been created and several techniques have been used with the objective of improving the discovery of the frequent sequence set. In this paper we present the main characteristics of some of the most important sequential pattern mining algorithms. Also, we show a comparative performance study among these algorithms.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
