Title :
A Weighted Association Rules Mining Algorithm with Fuzzy Quantitative Constraints
Author :
Qibing Lu ; Buyun Sheng
Author_Institution :
Sch. of Mech. Eng., Wuhan Univ. of Technol., Wuhan, China
Abstract :
Along with production process automation and development of new products, manufacturing information in large quantity, contains more dimensions, in order to mine useful information from the manufacturing database, monitor and control manufacturing process effectively. A weighted association rules mining algorithm with fuzzy quantitative constraints (FQC-wed Apriori algorithm) is proposed in this paper. First, find association rules after database mining. Then, mine fuzzy association rules with fuzzy query. Last, find frequent item sets with the improved weighted association rules algorithm. Manufacturing process information can be mined and effectiveness of the mining algorithm can be evaluated. The algorithm is applied to manufacturing process information mining in discrete manufacturing industry.
Keywords :
data mining; fuzzy reasoning; manufacturing data processing; query processing; FQC-wed Apriori algorithm; database mining; discrete manufacturing industry; frequent itemsets; fuzzy quantitative constraints; fuzzy query; manufacturing database; manufacturing process control; manufacturing process information mining; manufacturing process monitoring; mining algorithm effectiveness evaluation; new product development; production process automation; weighted association rule mining algorithm; Algorithm design and analysis; Association rules; Itemsets; Maintenance engineering; Manufacturing; association rules algorithm; data mining; fuzzy association; weighted support;
Conference_Titel :
Information Science and Cloud Computing Companion (ISCC-C), 2013 International Conference on
Conference_Location :
Guangzhou
DOI :
10.1109/ISCC-C.2013.34
