Circular Clustering With Polar Coordinate Reconstruction.

ABSTRACT: There is a growing interest in characterizing circular data found in biological systems. Such data are wide-ranging and varied, from the signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering algorithms are often inadequate due to their limited ability to distinguish differences in the periodic component θ. Current clustering schemes for polar coordinate systems have limitations, such as being only angle-focused or lacking generality. To overcome these limitations, we propose a new analysis framework that utilizes projections onto a cylindrical coordinate system to represent objects in a polar coordinate system optimally. Using the mathematical properties of circular data, we show that our approach always finds the correct clustering result withi

SUBMITTER: Sun X 

PROVIDER: S-EPMC11568864 | biostudies-literature | 2024 Sep-Oct

REPOSITORIES: biostudies-literature