Name
Abstract | ||
|---|---|---|
Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining WARM was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.4018/jdwm.2011070102 | IJDWM |
Keywords | Field | DocType |
weighting scheme result,authors experiment,item weighting,infers item weight,weighted association rule mining,novel valency model,association rule mining,weighting scheme,informative rule,automatic item weight generation,individual item,pattern mining,principal components analysis | Data mining,Weighting,Information retrieval,Computer science,Uniform distribution (continuous),Association rule learning,Artificial intelligence,Database transaction,A-weighting,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
7 | 3 | 1548-3924 |
Citations | PageRank | References |
10 | 0.51 | 32 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Russel Pears | 1 | 205 | 27.00 |
| Yun Sing Koh | 2 | 393 | 39.52 |
| Gill Dobbie | 3 | 728 | 77.75 |