Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Semi-supervised clustering is a new learning method which combines semi-supervised
learning (SSL) and cluster analysis. It is widely valued and applied to machine learning …

Data clustering is the process of dividing data elements into different clusters in which
elements in one cluster have more similarity than those in other clusters. Semi-supervised …

Recently, safe semi-supervised clustering (S3C) has become an emerging topic in machine
learning field. S3C aims to reduce the performance degradation probability of wrong prior …

Aiming at existing symmetric regularized picture fuzzy clustering with weak robustness, and
it is difficult to meet the need for image segmentation in the presence of high noise. Hence, a …

The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-
means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of …

A semi-supervised fuzzy c-means algorithm uses auxiliary class distribution knowledge and
fuzzy logic to handle semi-supervised clustering problems, named semi-supervised fuzzy c …

In this paper, we propose a safe semi-supervised clustering algorithm based on Dempster–
Shafer (D–S) evidence theory. The motivation is that D–S evidence theory can be used to …

Data partition with high confidence is one of the main concentration of researchers in Soft
Computing for many years. It is known that there may be some data with less confidence …

In this paper, we propose confidence-weighted safe semi-supervised clustering where prior
knowledge is given in the form of class labels. In some applications, some samples may be …