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  2013, Vol. 26 Issue (10): 975-984    DOI:
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Transfer Generalized Fuzzy C-Means Clustering Algorithm with Improved Fuzzy Partitions by Leveraging Knowledge
JIANG Yi-Zhang, DENG Zhao-Hong, WANG Jun, GE Hong-Wei, WANG Shi-Tong
School of Digital Media, Jiangnan University, Wuxi 214122

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Abstract  

To weaken the influence of the insufficient datasets and noises on the clustering analysis, a clustering algorithm, transfer generalized fuzzy C-means with improved fuzzy partitions (T-GIFP-FCM) is proposed based on the FCM framework-based clustering algorithm GIFP-FCM. By leveraging the historical knowledge in the related scene (domain) , the performance of T-GIFP-FCM is enhanced. Even if the data in the current scene are not enough, the promising clustering results can be obtained. The experimental results show the proposed algorithm has better performance compared with the traditional algorithms in situations of insufficient data.

Key wordsTransfer Learning      Insufficient Dataset      Fuzzy C-Means(FCM)      Generalized FCM with Improved Fuzzy Partitions(GIFP-FCM)     
Received: 08 November 2012     
ZTFLH: TP181  
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Articles by authors
JIANG Yi-Zhang
DENG Zhao-Hong
WANG Jun
GE Hong-Wei
WANG Shi-Tong
Cite this article:   
JIANG Yi-Zhang,DENG Zhao-Hong,WANG Jun等. Transfer Generalized Fuzzy C-Means Clustering Algorithm with Improved Fuzzy Partitions by Leveraging Knowledge[J]. , 2013, 26(10): 975-984.
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