Name
Abstract | ||
|---|---|---|
Weighted Association Rule Mining (WARM) is a technique that is commonly used to overcome the well-known limitations of the classical Association Rule Mining approach. The assignment of high weights to important items enables rules that express relationships between high weight items to be ranked ahead of rules that only feature less important items. Most previous research to weight assignment has used subjective measures to assign weights and are reliant on domain specific information. Whilst there have been a few approaches that automatically deduce weights from patterns of interaction between items, none of them take advantage of the situation where weights of only a subset of items are known in advance. We propose a model, WeightTransmitter, that interpolates the unknown weights from a known subset of weights. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-30220-6_4 | PAKDD (2) |
Keywords | Field | DocType |
deduce weight,express relationship,high weight,classical association rule mining,weighted association rule mining,landmark weight,known subset,important item,unknown weight,domain specific information,high weight item,association rule mining | Data mining,Ranking,Computer science,Specific-information,Association rule learning,Artificial intelligence,Landmark,High weight,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.44 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Sing Koh | 1 | 393 | 39.52 |
| Russel Pears | 2 | 205 | 27.00 |
| Gill Dobbie | 3 | 728 | 77.75 |