Thabtah, Fadi Abdeljaber, Cowling, Peter and Peng, Y. (2005) MCAR: multi-class classification based on association rule. The 3rd ACS/IEEE International Conference on Computer Systems and Applications, 2005. p. 33.Metadata only available from this repository.
Summary form only given. Constructing fast, accurate classifiers for large data sets is an important task in data mining and knowledge discovery. In this research paper, a new classification method called multi-class classification based on association rules (MCAR) is presented. MCAR uses an efficient technique for discovering frequent items and employs a rule ranking method which ensures detailed rules with high confidence are part of the classifier. After experimentation with fifteen different data sets, the results indicated that the proposed method is an accurate and efficient classification technique. Furthermore, the classifiers produced are highly competitive with regards to error rate and efficiency, if compared with those generated by popular methods like decision trees, RIPPER and CBA.
|Subjects:||T Technology > T Technology (General)|
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
|Schools:||School of Computing and Engineering|
|Depositing User:||Briony Heyhoe|
|Date Deposited:||06 Aug 2008 16:40|
|Last Modified:||22 Oct 2008 10:55|
Repository Staff Only: item control page