We propose the seed-and-extension-based density peaks (SDP) algorithm which incorporates a new center selection strategy into the famous density peaks (DP) algorithm [Rodriguez and Laio, Science, 344(6191): 1492-1496, 2014]. In particular, SDP selects the centers that hold the features of their clusters while building a spanning forest, and meanwhile, constructs the output clusters in a seed-and-extension manner. SDP is more accurate than existing clustering approaches for a variety of types of datasets, including time-series data. We believe that SDP would be helpful to unsupervised learning as well as many applications and practical problems.
Our software is available freely for non-commercial purposes here:
If you use SDP, please cite this paper:
Ming-Hao Tung, Yi-Ping Phoebe Chen, Chen-Yu Liu and Chung-Shou Liao. (2022) A Fast and More Accurate Seed-and-Extension Density-based Clustering Algorithm, IEEE Transactions on Knowledge and Data Engineering (TKDE), published online, 2022. DOI: 10.1109/TKDE.2022.3161117