Title :
Improved GA-based text clustering algorithm
Author :
Shi, Kansheng ; Li, Lemin ; He, Jie ; Zhang, Naitong ; Liu, Haitao ; Song, Wentao
Author_Institution :
Shanghai Jiaotong Univ., Shanghai, China
Abstract :
The traditional K-means algorithm is sensitive to the initial points and easy to fall into local optimum. To avoid this kind of flaw, an improved GA-based text clustering algorithm CGHCM is proposed. The new algorithm is proven effective to avoid falling into local optimum and obtains better clustering results.
Keywords :
genetic algorithms; pattern clustering; text analysis; unsupervised learning; GA-based text clustering algorithm; K-means algorithm; genetic algorithm; unsupervised machine learning method; Accuracy; Algorithm design and analysis; Biological cells; Clustering algorithms; Genetic algorithms; Mathematical model; Vectors; GA; K-means; Similarity measurement; VSM; text clustering;
Conference_Titel :
Broadband Network and Multimedia Technology (IC-BNMT), 2011 4th IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-61284-158-8
DOI :
10.1109/ICBNMT.2011.6156021
