Traditional image clustering methods take a two-step approach, feature learning and
clustering, sequentially. However, recent research results demonstrated that combining the …

Density based clustering methods are proposed for clustering spatial databases with noise.
Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover …

Density peaks clustering (DPC) is as an efficient clustering algorithm due for using a non-
iterative process. However, DPC and most of its improvements suffer from the following …

This paper presents an effective scene classification approach based on collaborative
representation fusion of local and global spatial features. First, a visual word codebook is …

Matrix factorization is an important technology that obtains the latent representation of data
by mining the potential structure of data. As two popular matrix factorization techniques …

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores
without access to reference images. State-of-the-art BIQA methods typically require subjects …

DPC (clustering by fast search and find of density peaks) is an efficient clustering algorithm.
However, DPC and its variations usually cannot detect the appropriate cluster centers for a …

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped
clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively …

Many modern clustering models can be divided into two separated steps, ie, constructing a
similarity graph (SG) upon samples and partitioning each sample into the corresponding …

DBSCAN is one of clustering algorithms which can report arbitrarily-shaped clusters and
noises without requiring the number of clusters as a parameter (unlike the other clustering …