Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Clustering is central to many data-driven bioinformatics research and serves a powerful
computational method. In particular, clustering helps at analyzing unstructured and high …

The advent of architectures such as the Internet of Things (IoT) has led to the dramatic
growth of data and the production of big data. Managing this often-unlabeled data is a big …

Accurate estimation of the traffic state can help to address the issue of urban traffic
congestion, providing guiding advices for people's travel and traffic regulation. In this paper …

Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been
successfully used to solve numerous complex optimization problems. This paper proposed a …

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is
useful in a wide variety of applications, including document processing and modern …

Text document clustering (TDC) represents a key task in text mining and unsupervised
machine learning, which partitions a specific documents' collection into varied K-groups …

Clustering face images according to their latent identity has two important applications: 1)
grouping a collection of face images when no external labels are associated with images …

In this article, we suggest a novel deep learning recommendation framework that
incorporates contextual information into neural collaborative filtering recommendation …

Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …