Name
Abstract | ||
|---|---|---|
Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing a novel Shannon-like Entropy, we aggregate and measure the information contained in an item with the coexistence of fuzzy uncertainty hidden in quantitative values and random uncertainty. We then propose Support and Confidence metrics for a fuzzy-probabilistic database to quantify association rules. Finally, we design an algorithm, called FARP (mining Fuzzy Association Rules from a Probabilistic quantitative data), to discover frequent fuzzy-probabilistic itemsets and fuzzy association rules using the proposed interest measures. The experimental results show the effectiveness of our method and its practicality in real-world applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ins.2013.02.010 | Inf. Sci. |
Keywords | Field | DocType |
association rule mining focus,boolean association rule,quantitative value,probabilistic quantitative data,quantitative information,probabilistic quantitative databases,fuzzy association rule,quantitative association rule,association rule,mining association rule | Data mining,Apriori algorithm,Measurement uncertainty,Association rule learning,Artificial intelligence,Probabilistic logic,Fuzzy uncertainty,Fuzzy association rules,Mathematics,Database,Machine learning,Probabilistic database | Journal |
Volume | ISSN | Citations |
237, | 0020-0255 | 15 |
PageRank | References | Authors |
0.59 | 55 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Pei | 1 | 15 | 1.60 |
| Suyun Zhao | 2 | 585 | 20.33 |
| Hong Chen | 3 | 99 | 23.20 |
| Xuan Zhou | 4 | 57 | 2.00 |
| Ding-Jie Chen | 5 | 31 | 6.70 |