Yu Jiang1, 2, Dengwen Yu1, Mingzhao Zhao1, 2, Hongtao Bai1, 2, Chong Wang1, 2, 3, Lili He1, 2, *
CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 207-216, 2020, DOI:10.32604/cmc.2020.09861
Abstract Semi-supervised clustering improves learning performance as long as it uses a
small number of labeled samples to assist un-tagged samples for learning. This paper
implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two
classical clustering algorithms. The Election-AP algorithm is proposed to handle the final
cluster number in AP clustering as it has proved to be difficult to control in a suitable
range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to
the thermocline standard definition, and use this data for semi-supervised cluster analysis.
Several semi-supervised clustering… More >
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