Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Density peak clustering (DPC) algorithm has become a well-known clustering method
during the last decade, The research communities believe that DPC is a powerful tool …

With the increasing data scale in the Industrial Internet of Things, edge computing
coordinated with machine learning is regarded as an effective way to raise the novel latency …

Partial label learning refers to the issue that each training sample corresponds to a
candidate label set containing only one valid label. Feature selection can be viewed as an …

Label distribution learning is a widely studied supervised learning diagram that can handle
the problem of label ambiguity. The increasing size of datasets is accompanied by the …

Partial multilabel learning (PML) has attracted considerable interest from scholars. Most
PML models construct objective functions and optimize target parameters, which add noise …

Standard k-means clustering necessitates computing pairwise Euclidean distances between
each instance x in a data set D and all cluster centers, resulting in inadequate efficiency …

The explosive growth of datasets is always accompanied by dimension disasters, which
have become more common in multi-label data. Various feature selection techniques are …

The k-means, one of the most widely used clustering algorithm, is not only faster in
computation but also produces comparatively better clusters. However, it has two major …

Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common
rough set theoretical models. Although the PRS can use equivalence classes to represent …