Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

The wide application of smart devices enables the availability of multimodal data, which can
be utilized in many tasks. In the field of multimodal sentiment analysis, most previous works …

Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying
underlying network structures. Recently, a variety of graph representation learning models …

In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic
algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest …

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …

Graph clustering, which learns the node representations for effective cluster assignments, is
a fundamental yet challenging task in data analysis and has received considerable attention …

The modern agriculture industry is data-centred, precise and smarter than ever. Advanced
development of Internet-of-Things (IoT) based systems redesigned “smart agriculture”. This …

Traditional partition-based clustering is very sensitive to the initialized centroids, which are
easily stuck in the local minimum due to their nonconvex objectives. To this end, convex …

Traditional quantum-based evolutionary algorithms are intended to solve single-objective
optimization problems or multiobjective small-scale optimization problems. However …

Designing suitable convolutional neural networks (CNNs) for different image data requires
much human effort and expertise, in recent years, this process has been greatly accelerated …