Name
Abstract | ||
|---|---|---|
Much of the work in the data mining community mines temporal knowledge based primarily on the frequency of events, e.g., frequent pattern mining, ignoring their duration. This paper discusses a method that mines big learning data by taking both the frequency and duration into account. It defines a function for evaluating the importance of events, summarizing them into big uniform events (BUEs) according to the semantics, and further segmenting the BUEs using a sliding window to avoid the counting bias issue. The task of finding temporal characteristics is eventually reduced to mining complex temporally frequent patterns and association rules. To validate this method, a series of extensive experiments are conducted on both synthetic and real datasets to test the system overhead, quality of patterns, and model parameters. The results show that our mining framework is serviceable and can effectively improve the quality of patterns. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2018.03.018 | Information Sciences |
Keywords | Field | DocType |
Temporal data mining,Temporal characteristics,Interval events,E-learning | E learning,Sliding window protocol,Market segmentation,Association rule learning,Artificial intelligence,Semantics,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
447 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 31 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Xie | 1 | 1 | 0.69 |
| Qinghua Zheng | 2 | 1261 | 160.88 |
| Weizhan Zhang | 3 | 101 | 18.64 |