Name
Abstract | ||
|---|---|---|
Research inWeighted Association Rule Mining (WARM) has largely concentrated on mining traditional static transactional datasets. Whilst there have been a few attempts at researching WARM in a data stream environment, none have addressed the problem of assigning and adapting weights in the presence of concept drift, which often occurs in a data stream environment. In this research we experiment with two methods of adapting weights; firstly, a simplistic method that recomputes the entire set of weights at fixed intervals, and secondly a method that relies on a distance function that assesses the extent of change in the stream and only updates those items that have had significant change in their patterns of interaction. We show that the latter method is able to maintain good accuracy whilst being several times faster than the former. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-20841-6_32 | PAKDD |
Keywords | Field | DocType |
automatic assignment,data stream environment,significant change,entire set,concept drift,association rule mining,latter method,fixed interval,simplistic method,pattern mining,distance function,item weight,good accuracy whilst,data stream mining | Data mining,Data stream mining,Computer science,Data stream,Metric (mathematics),Concept drift,Association rule learning,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
6634 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Sing Koh | 1 | 393 | 39.52 |
| Russel Pears | 2 | 205 | 27.00 |
| Gill Dobbie | 3 | 728 | 77.75 |