Title :
A New Approach of Self-adaptive Discretization to Enhance the Apriori Quantitative Association Rule Mining
Author :
Li Dancheng ; Zhang Ming ; Zhou Shuangshuang ; Zheng Chen
Author_Institution :
Northeastern Univ., Shenyang, China
Abstract :
Apriori algorithm was widely applied in association rule mining. Generally, we have to specify different ranges manually to discretize numeral fields to nominal fields, which may weaken the result due to unfit partitions. This paper introduced an approach to make discretized partitions in a self adaptive way to enhance the numeral quantitative association rule mining result.
Keywords :
data mining; apriori quantitative association rule mining; data mining; nominal fields; numeral fields; self adaptive discretization; Art; Association rules; Dictionaries; Itemsets; Partitioning algorithms; Planning; Apriori algorithm; Association rule mining; Self-adapting discretization;
Conference_Titel :
Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-1-4577-2120-5
DOI :
10.1109/ISdea.2012.540
