DocumentCode
130488
Title
Intelligent association rules for innovative SME collaboration
Author
Kayakutlu, Gulgun ; Duzdar, Irem ; Mercier-Laurent, Eunika
Author_Institution
Ind. Eng. Dept., Istanbul Tech. Univ., Macka, Turkey
fYear
2014
fDate
7-10 Sept. 2014
Firstpage
1391
Lastpage
1396
Abstract
SMEs are encouraged to collaborate for research and innovation in order to survive in tough global competition. Even the technology SMEs with high knowledge capital have the fear to collaborate with other SMEs or bigger companies. This study aims to illuminate the preferences in customer, supplier and competitor collaboration within industry or inter industry. A survey is run on more than 110 companies and Machine Learning methods are used to define the association rules that will lead for success.
Keywords
data mining; innovation management; knowledge management; learning (artificial intelligence); small-to-medium enterprises; customer-supplier-competitor collaboration; global competition; innovative SME collaboration; intelligent association rules; knowledge capital; machine learning methods; research and innovation; technology SMEs; Association rules; Collaboration; Companies; Industries; Support vector machines; Technological innovation; Association Rules; Collaborative Innovation; SOM; SVM;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on
Conference_Location
Warsaw
Type
conf
DOI
10.15439/2014F289
Filename
6933180
Link To Document