The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

The alternating direction method with multipliers (ADMM) is one of the most powerful and
successful methods for solving various composite problems. The convergence of the …

Semi-supervised clustering is one of important research topics in cluster analysis, which
uses pre-given knowledge as constraints to improve the clustering performance. While …

This research work discusses a noise-robust picture fuzzy clustering method with an
application to the MRI image segmentation problem. The MRI images suffer from the …

Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …

Conventional semi-supervised clustering approaches have several shortcomings, such as
(1) not fully utilizing all useful must-link and cannot-link constraints,(2) not considering how …

Traditional cluster ensemble approaches have several limitations.(1) Few make use of prior
knowledge provided by experts.(2) It is difficult to achieve good performance in high …

High dimensional data classification with very limited labeled training data is a challenging
task in the area of data mining. In order to tackle this task, we first propose a feature …

Electroencephalogram (EEG)-based applications in Brain-Computer Interfaces (BCIs, or
Human-Machine Interfaces, HMIs), diagnosis of neurological disease, rehabilitation, etc, rely …

As an exemplar-based clustering method, the well-known density peaks clustering (DPC)
heavily depends on the computation of kernel-based density peaks, which incurs two issues …