Name
Abstract | ||
|---|---|---|
This paper proposes a Bayesian association rule mining algorithm (BAR) which combines the Apriori association rule mining algorithm with Bayesian networks. Two interesting-ness measures of association rules: Bayesian confidence (BC) and Bayesian lift (BL) which measure conditional dependence and independence relationships between items are defined based on the joint probabilities represented by the Bayesian networks of association rules. BAR outputs best rules according to BC and BL. BAR is evaluated for its performance using two anonymized clinical phenotype datasets from the UCI Repository: Thyroid disease and Diabetes. The results show that BAR is capable of finding the best rules which have the highest BC, BL and very high support, confidence and lift. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SMC.2013.555 | Systems, Man, and Cybernetics |
Keywords | Field | DocType |
best rule,uci repository,bayesian network,apriori association rule mining,bayesian confidence,highest bc,bayesian lift,association rule,bayesian association rule mining,bar outputs best rule,data mining,probability | Data mining,Computer science,Artificial intelligence,Bayesian statistics,Variable-order Bayesian network,Bayes factor,Algorithm,Association rule learning,Bayesian network,Bayesian hierarchical modeling,Chain rule (probability),Graphical model,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 1 | 0.35 |
References | Authors | |
8 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Tian | 1 | 23 | 3.17 |
| Ann Gledson | 2 | 14 | 2.40 |
| Athos Antoniades | 3 | 6 | 2.90 |
| Aristo Aristodimou | 4 | 1 | 0.35 |
| Ntalaperas Dimitrios | 5 | 20 | 3.23 |
| Ratnesh Sahay | 6 | 69 | 15.66 |
| Jianxin Pan | 7 | 25 | 6.67 |
| Stavros Stivaros | 8 | 1 | 0.35 |
| Goran Nenadic | 9 | 228 | 13.18 |
| Xiao-jun Zeng | 10 | 2282 | 125.89 |
| John Keane | 11 | 59 | 6.17 |