Name
Abstract | ||
|---|---|---|
Since there are many real-life situations in which people are uncertain about the content of transactions, association rule
mining with uncertain data is in demand. Most of these studies focus on the improvement of classical algorithms for frequent
itemsets mining. To obtain a tradeoff between the accuracy and computation time, in this paper we introduces an efficient
algorithm for finding association rules from uncertain data with sampling-SARMUT, which is based on the FAST algorithm introduced
by Chen et al. Unlike FAST, SARMUT is designed for uncertain data mining. In response to the special characteristics of uncertainty,
we propose a new definition of ”distance” as a measure to pick representative transactions. To evaluate its performance and
accuracy, a comparison against the natural extension of FAST is performed using synthetic datasets. The experimental results
show that the proposed sampling algorithm SARMUT outperforms FAST algorithm, and achieves up to 97% accuracy in some cases.
|
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16530-6_16 | artificial intelligence and computational intelligence |
Keywords | DocType | Volume |
fast algorithm,proposed sampling algorithm,classical algorithm,uncertain data,computation time,frequent itemsets mining,association rule mining,association rule,efficient algorithm,uncertain data mining | Conference | 6319 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-16529-X | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhu Qian | 1 | 0 | 0.34 |
| Donghua Pan | 2 | 23 | 5.53 |
| Yang Guangfei | 3 | 0 | 0.34 |