It remains challenging in characterizing uncertain and imprecise information when clustering
fuzzy number data. To solve such a problem, this paper investigates a new credal-based …

With the advancement of microarray technology, gene expression profiling has shown
remarkable effort to predict the different types of malignancy and their subtypes. In …

Automatic recognition of bedridden patients' physical activity has important applications in
the clinical process. Such recognition tasks are usually accomplished on visual data …

As a common technology in social network, clustering has attracted lots of research interest
due to its high performance, and many clustering methods have been presented. The most …

Microarray technology has developed rapidly in recent years, producing a large number of
ultra-high dimensional gene expression data. However, due to the huge sample size and …

为使AP 算法对图像进行聚类时充分考虑不同尺度的特征及有效利用未标记数据的特征,
提出了结合特征金字塔网络的半监督AP 聚类算法(Semi-supervised AP clustering Based on …

It is often tricky in real-life classification applications to select model features that would
ensure an adequate sample classification, given a large number of candidate features. Our …

One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides
k-means and hierarchical clustering. Since FCM treats all data features as equally important …

Semi-supervised clustering has attracted lots of reserach interest due to its broad
applications, and many methods have been presented. However there is still much space for …

Deep embedding clustering (DEC) attracts much attention due to its outperforming
performance attributed to the end‐to‐end clustering. However, DEC cannot make use of …