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. <\/p><\/p>\n\n\n\n
Our software is available freely for non-commercial purposes here:<\/p>\n\n\n\n
If you use SDP, please cite this paper<\/a>:<\/p>\n\n\n\n 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<\/p><\/p>\n\n\n\n If you have any questions or feedback, please contact nthu.acolab@ie.nthu.edu.tw<\/a> and csliao@ie.nthu.edu.tw<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" 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 … Continue reading Contact<\/h3>\n\n\n\n