Universal clustering algorithm based on an adaptive density gradient
We proposed a universal clustering algorithm by constructing an adaptive density gradient. This algorithm showed no data preference, and performs well on data with arbitrary density, overlap and shape. In comparative experiments, it outperformed other state-of-the-art algorithms on all types of synthetic and real data.
Fundamental Research Funds for the Central Universi-ties (3332020019)
CAMS Innovation Fund for Medical Sciences ( 2021-I2M-1-008)
Email Address of Submitting Authorwk1lian@126.com
ORCID of Submitting Author0000-0003-0984-9468
Submitting Author's InstitutionFuwai hospital
Submitting Author's Country