Title :
A Classification Algorithm Based on an Association Rule of Multiple Frequent Item-Sets
Author_Institution :
Software Coll., Shenyang Normal Univ., Shenyang, China
Abstract :
It is necessary to discrete datasets firstly if you want to data mining an association rule of datasets consisting of many categorical and numeric attributes by a traditional algorithm. However, in view of the versatility, the applications of the traditional algorithm are limited. This paper propose a new algorithm called ARMFI(Association Rule of Multiple Frequent Item-sets) which can data mining an Association Rule from datasets consisting of many categorical and numeric attributes directly and completely, and overcome disadvantage of the traditional algorithm. The result has been proofed that the ARMFI shows better performances than the traditional algorithm.
Keywords :
data mining; pattern classification; set theory; association rule; classification algorithm; data mining; multiple frequent item-set; numeric attribute; Association rules; Classification algorithms; Classification tree analysis; Data mining; Decision trees; Educational institutions; Electronic mail; Hybrid intelligent systems; Itemsets; Software algorithms; ARMFI; Classification; Data mining; Frequent Item-sets; Frequent Regions;
Conference_Titel :
Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-0-7695-3745-0
DOI :
10.1109/HIS.2009.271