Circular Clustering with Polar Coordinate Reconstruction
This work addresses the challenge of clustering circular data for researchers in fields like biology, where traditional methods are inadequate, though it appears incremental as it builds on existing polar coordinate-based approaches.
The authors tackled the problem of clustering circular data, which is common in biological systems, by proposing a new analysis framework using cylindrical coordinate projections. They demonstrated that their method produces more appropriate and consistent clustering results compared to standard methods on synthetic and real data.
There is a growing interest in characterizing circular data found in biological systems. Such data are wide ranging and varied, from 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. Current clustering schemes that work in a polar coordinate system 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 better represent objects in a polar coordinate system. Using the mathematical properties of circular data, we show our approach always finds the correct clustering result within the reconstructed dataset, given sufficient periodic repetitions of the data. Our approach is generally applicable and adaptable and can be incorporated into most state-of-the-art clustering algorithms. We demonstrate on synthetic and real data that our method generates more appropriate and consistent clustering results compared to standard methods. In summary, our proposed analysis framework overcomes the limitations of existing polar coordinate-based clustering methods and provides a more accurate and efficient way to cluster circular data.