Transfer Learning for Entropy-Weighted Fuzzy Clustering
- Resource Type
- Conference
- Authors
- Dang, Bozhan; Zhou, Jin; Wang, Yingxu; Xu, Guangmei; Wang, Dong; Wang, Lin; Han, Shiyuan; Chen, Yuehui
- Source
- 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Security, Pattern Analysis, and Cybernetics (SPAC), 2018 International Conference on. :164-168 Dec, 2018
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Clustering algorithms
Linear programming
Partitioning algorithms
Task analysis
Classification algorithms
Power capacitors
Security
transfer learning
entropy-weighted fuzzy c-means clustering
clustering centers
weights of dimensions
- Language
The traditional clustering algorithms can not effectively deal with the clustering when the data for current task are not enough. In this paper, we utilize transfer learning to assist the entropy-weighted fuzzy c-means clustering. The clustering centers and corresponding weights of dimensions learned from the known data domain are used in the new objective function to assist the unknown data clustering. Experiments on synthetic data sets have demonstrated the superiority of the new algorithm.