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
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;
Conference_Titel :
Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on
Conference_Location :
Warsaw
