Name
Abstract | ||
|---|---|---|
The relevance of traditional classification methods, such as CBA and CMAR, bring the problems of frequent scanning the database, resulting in excessive candidate sets, as well as the complex construction of FP-tree that causes excessive consumption. This paper studies the classification rules based on association rules - MCAR (Mining Class Association Rules). The database only needs scanning once, and the cross-support operation is used for the calculation as the format of databases is vertical layout for easily computing the support of the frequent items. Not only the minimum support and minimum confidence is used to prune the candidate set, but also the concept of class-frequent items is taken into account to delete the rules that may hinder the effective improvement of the algorithm performance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04843-2_29 | ISICA |
Keywords | Field | DocType |
excessive consumption,frequent item,excessive candidate set,mining class association rules,algorithm performance,minimum support,traditional classification method,minimum confidence,classification rule,candidate set,data mining,association rule | Data mining,Apriori algorithm,Algorithm,Association rule learning,Artificial intelligence,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
5821 | 0302-9743 | 4 |
PageRank | References | Authors |
0.41 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Man Zhao | 1 | 4 | 1.43 |
| Xiu Cheng | 2 | 4 | 0.41 |
| Qianzhou He | 3 | 4 | 0.41 |