Attributed graph clustering aims to group nodes into disjoint categories using deep learning
to represent node embeddings and has shown promising performance across various …

A clustering ensemble provides an elegant framework to learn a consensus result from
multiple prespecified clustering partitions. Though conventional clustering ensemble …

In the traditional manufacturing industry, the safe operation of storage and retrieval (S/R)
machines is vital for efficient automated warehouse management. Recently, deep learning …

The application of clustering has always been an important method for problem-solving. In
the era of big data, most classical clustering methods suffer from the curse of dimensionality …

Deep multi-view clustering has been widely studied. However, since it is an unsupervised
task, where no labels are used to guide the training, it is still unreliable especially when …

The recognition and clustering of deformation modes are key to constructing impact
deformation constraints for thin-walled structures. This paper transforms the clustering and …

Clustering is fundamentally a subjective task: a single dataset can be validly clustered in
various ways, and without further information, clustering systems cannot determine the …

We present a novel clustering algorithm, visClust, that is based on lower dimensional data
representations and visual interpretation. Thereto, we design a transformation that allows …

In response to the structural changes of tomato seedlings, traditional image techniques are
difficult to accurately quantify key morphological parameters, such as leaf area, internode …

Deep Multi-task image clustering endeavors to leverage deep learning techniques for the
simultaneous processing of multiple clustering tasks. Current multi-task deep image …