Transfer Learning Based Kernel Fuzzy Clustering

Dang, Bozhan; Zhou, Jin; Liu, Xiangdao; Wang, Rongrong; Wang, Lin; Han, Shiyuan; Chen, Yuehui

doi:10.1109/ifuzzy46984.2019.9066208

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past decade or so, many unsupervised transfer clustering algorithms have been developed by combining clustering algorithms with transfer learning. According to the transfer method, transfer clustering algorithms can be roughly divided into four categories [22]: instance-based [23], feature-representation-based [24,25], parameter-based [26][27][28][29], relational knowledge-based [30]. The earliest study is the self-taught clustering (STC) based on mutual information proposed by Dai et al [24].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution-based transfer rough clustering algorithm

Zhao

Wang

Liu

2023

Complex Intell. Syst.

View full text Add to dashboard Cite

Due to well processing the uncertainty in data, rough clustering methods have been successfully applied in many fields. However, when the capacity of the available data is limited or the data are disturbed by noise, the rough clustering algorithms always cannot effectively explore the structure of the data. Furthermore, rough clustering algorithms are usually sensitive to the initialized cluster centers and easy to fall into local optimum. To resolve the problems mentioned above, a novel differential evolution-based transfer rough clustering (DE-TRC) algorithm is proposed in this paper. First, transfer learning mechanism is introduced into rough clustering and a transfer rough clustering framework is designed, which utilizes the knowledge from the related domain to assist the clustering task. Then, the objective function of the transfer rough clustering algorithm is optimized by using the differential evolution algorithm to enhance the robustness of the algorithm. It can overcome the sensitivity to initialized cluster centers and meanwhile achieve the global optimal clustering. The proposed algorithm is validated on different synthetic and real-world datasets. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with both traditional rough clustering algorithms and other state-of-the-art clustering algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Differential evolution-based transfer rough clustering algorithm

Zhao

Wang

Liu

2023

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm

Zhao,

Yang,

Liu

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images.

show abstract