
Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal patterns, which describe the temporal behavior of Web users. Clusters and associations in Web usage mining do not necessarily have crisp boundaries. We introduce the temporal Web usage mining of Web users on one educational Web site, using the adapted Kohonen SOM based on rough set properties [L. J. Pawan et al. (2002)].
| 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). | 8 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
