Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Deep clustering learns deep feature representations that favor clustering task using neural
networks. Some pioneering work proposes to simultaneously learn embedded features and …

Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …

Deep clustering utilizes deep neural networks to learn feature representation that is suitable
for clustering tasks. Though demonstrating promising performance in various applications …

Clustering is central to many data-driven application domains and has been studied
extensively in terms of distance functions and grouping algorithms. Relatively little work has …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

Technological advancement has enabled us to store and process huge amount of data in
relatively short spans of time. The nature of data is rapidly changing, particularly its …