TECM: Transfer Evidential C-means Clustering

by   Lianmeng Jiao, et al.

Clustering is widely used in text analysis, natural language processing, image segmentation, and other data mining fields. As a promising clustering algorithm, the evidential c-means (ECM) can provide a deeper insight on the data by allowing an object to belong to several subsets of classes, which extends those of hard, fuzzy, and possibilistic clustering. However, as it needs to estimate much more parameters than the other classical partition-based algorithms, it only works well when the available data is sufficient and of good quality. In order to overcome these shortcomings, this paper proposes a transfer evidential c-means (TECM) algorithm, by introducing the strategy of transfer learning. The objective function of TECM is obtained by introducing barycenters in the source domain on the basis of the objective function of ECM, and the iterative optimization strategy is used to solve the objective function. In addition, the TECM can adapt to situation where the number of clusters in the source domain and the target domain is different. The proposed algorithm has been validated on synthetic and real-world datasets. Experimental results demonstrate the effectiveness of TECM in comparison with the original ECM as well as other representative multitask or transfer clustering algorithms.


page 3

page 4

page 5

page 6

page 7

page 8

page 10

page 14


Fuzzy clustering algorithms with distance metric learning and entropy regularization

The clustering methods have been used in a variety of fields such as ima...

Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

Lesion segmentation of ultrasound medical images based on deep learning ...

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

Clustering is an effective technique in data mining to group a set of ob...

Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning

Clustering uncertain data is an essential task in data mining for the in...

Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching

Deep text matching approaches have been widely studied for many applicat...

Transfer Learning via ℓ_1 Regularization

Machine learning algorithms typically require abundant data under a stat...

Searching for network modules

When analyzing complex networks a key target is to uncover their modular...

Please sign up or login with your details

Forgot password? Click here to reset