Fast and efficient are common requirements for all clustering algorithms. Density peaks
clustering algorithm (DPC) can deal with non-spherical clusters well. However, due to the …

Detecting fuzzy communities in networks is a critical but challenging task in many fields
including biology, technology, social system and so on. Current technology is largely reliant …

Clustering is a task used to group data from variegated sources, including Big Data, the
Internet of Things, and social media. Density peaks clustering (DPC) has become a popular …

Most clustering algorithms rely on the assumption that data simply contains numerical
values. In fact, however, data sets containing both numerical and categorical attributes are …

Density peaks clustering (DPC) algorithm is a succinct and efficient density-based clustering
approach to data analysis. It computes the local density and the relative distance for objects …

Tieguanyin tea plant is the most important tea cultivar in Fujian Province, China. It has
suffered great economic losses due to high temperature and dry weather in recently years …

Clustering is an important unsupervised learning method in machine learning and data
mining. Many existing clustering methods may still face the challenge in self-identifying …

The fast search and find of density peaks clustering (FDP) is an algorithm that can gain
satisfactory clustering results with manual selection of the cluster centres. However, this …

A community is a group of people that shares something in common. The definition of the
community can be generalized as things that have common properties. By using this …

The growing scale of networks makes the study of social networks increasingly difficult.
Overlapping community detection can both make the network easier to analyze and manage …