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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.
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Received: 08 November 2012
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2013, Vol. 26 
