A Component Clustering Algorithm Based on Semantic Similarity and Optimization
| Autor: | Chen Li-chao, Xie Bin-hong, Ren Yaopeng, Zhang Yingjun |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Clustering high-dimensional data
Fuzzy clustering Computer science business.industry Correlation clustering Pattern recognition computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Data stream clustering Semantic similarity CURE data clustering algorithm Canopy clustering algorithm Data mining Artificial intelligence business Cluster analysis computer |
| Zdroj: | CASoN |
| DOI: | 10.1109/cason.2010.20 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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