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

In order to explore the application value of deep learning denoising autoencoder (DAE) in
Internet-of-Things (IoT) fusion security, in this study, a hierarchical intrusion security …

In order to protect industrial safety, improve the operation stability of the industrial control
system, conduct the response measures for network environment attacked by the external …

Semi-supervised self-labeled methods apply unlabeled data to improve the performance of
classifiers which are trained by labeled data alone. Nevertheless, applying unlabeled data …

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 …

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 …