[1]BIAN Zekang,WANG Shitong.Robust FCM clustering algorithm based on hybrid-distance learning[J].CAAI Transactions on Intelligent Systems,2017,12(4):450-458.[doi:10.11992/tis.201607019]
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Robust FCM clustering algorithm based on hybrid-distance learning
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 4
Page number:
450-458
Column:
学术论文—机器学习
Public date:
2017-08-25
- Title:
- Robust FCM clustering algorithm based on hybrid-distance learning
- Keywords:
- distance metric; FCM clustering algorithm; pairwise constraints; side information; hybrid distance; semi-supervised; GIFP-FCM; robustness
- CLC:
- TP181
- DOI:
- 10.11992/tis.201607019
- Abstract:
- The distance metric plays a vital role in the fuzzy C-means clustering algorithm. In actual applications, there is a practical scenario in which the clustered data have a certain amount of side information, such as pairwise constraints with labels. To sufficiently utilize this side information, first, we propose a learning method based on hybrid distance, in which side information can be utilized to attain a distance metric formula for the data set. Next, we propose a robust fuzzy C-means clustering algorithm (HR-FCM algorithm) based on hybrid-distance learning, which is semi-supervised. The HR-FCM inherits the robustness of the GIFP-FCM (generalized FCM algorithm with improved fuzzy partitions) and has better clustering performance due to the more appropriate distance metric. The experimental results confirm the effectiveness of the proposed algorithm.
- References:
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Last Update:
2017-08-25