Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Machine learning and data mining have established several effective applications in gene
selection analysis. This paper review semi-supervised learning algorithms and gene …

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 …

Human identification is vital in health monitoring, human-computer interaction, safety
detection, and other fields. Compared with traditional vision-based methods, millimeter wave …

Density peaks clustering (DPC) model focuses on searching density peaks and clustering
data with arbitrary shapes for machine learning. However, it is difficult for DPC to select a cut …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

Semi-supervised learning can be described from different perspectives, which plays a
crucial role in the study of machine learning. In this study, a new aspect of 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 …

Recently, both ensemble clustering and semi-supervised clustering have emerged as
important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple …

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 …