Name
Abstract | ||
|---|---|---|
In recent years, the importance of identifying actionable patterns has become increasingly recognized so that decision-support actions can be inspired by the resultant patterns. A typical shift is on identifying high utility rather than highly frequent patterns. Accordingly, High Utility Itemset (HUI) Mining methods have become quite popular as well as faster and more reliable than before. However, the current research focus has been on improving the efficiency while the coupling relationships between items are ignored. It is important to study item and itemset couplings inbuilt in the data. For example, the utility of one itemset might be lower than user-specified threshold until one additional itemset takes part in; and vice versa, an item's utility might be high until another one joins in. In this way, even though some absolutely high utility itemsets can be discovered, sometimes it is easily to find out that quite a lot of redundant itemsets sharing the same item are mined (e.g., if the utility of a diamond is high enough, all its supersets are proved to be HUIs). Such itemsets are not actionable, and sellers cannot make higher profit if marketing strategies are created on top of such findings. To this end, here we introduce a new framework for mining actionable high utility association rules, called Combined Utility-Association Rules (CUAR), which aims to find high utility and strong association of itemset combinations incorporating item/itemset relations. The algorithm is proved to be efficient per experimental outcomes on both real and synthetic datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DSAA.2015.7344840 | 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | Field | DocType |
high utility itemset mining,actionable combined pattern mining,association rule,pattern relation analysis | Data mining,Joins,Computer science,Association rule learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-8272-4 | 0 | 0.34 |
References | Authors | |
19 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyu Shao | 1 | 2 | 1.04 |
| Junfu Yin | 2 | 103 | 4.71 |
| Wei Liu | 3 | 468 | 37.36 |
| Longbing Cao | 4 | 2212 | 185.04 |