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 …

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 …

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 …

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 …

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 …

Feature Selection (FS) is the core part of data processing pipeline. Use of ensemble in FS is
a relatively new approach aiming at producing more diversity in feature dataset, which …