A Component Clustering Algorithm Based on Semantic Similarity and Optimization

• Published in: 2010 International Conference on Computational Aspects of Social Networks pp. 53 - 57
• Main Authors: Zhang Yingjun, Ren Yaopeng, Chen Lichao, Xie Binhong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2010
• Subjects: Clustering optimization; Component clustering; Decision support systems; Faceted classification; Frequency modulation; Full-text retrieval; Semantic similarity; Social network services
• ISBN: 9781424487851; 1424487854
• DOI: 10.1109/CASoN.2010.20

To overcome the subjective factors of faceted classification representation, the method combined the faceted classification with text retrieval is used to describe the components. Meanwhile, from the semantic view and combined optimization techniques, a component clustering algorithm based on semantic similarity and optimization is proposed. This algorithm can reduce the subjective factors of faceted classification, and further improve the efficiency and accuracy of component search. And compared with component clustering effect based on vector space model, the experiments prove that this component clustering algorithm based on semantic similarity and optimization is effective which can improve the result of component clustering and raise the clustering quality.