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

fYear

2013

fDate

7-8 Dec. 2013

Firstpage

481

Lastpage

487

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Cloud Computing Companion (ISCC-C), 2013 International Conference on

Conference_Location

Guangzhou

Type

conf

DOI

10.1109/ISCC-C.2013.34

Filename

6973639