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Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

Yu Jiang1, 2, Dengwen Yu1, Mingzhao Zhao1, 2, Hongtao Bai1, 2, Chong Wang1, 2, 3, Lili He1, 2, *

1 College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
2 A Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, 130012, China.
3 Department of Engineering Mechanics, State Marine Technical University of St. Petersburg, St. Petersburg, 190008, Russia.

* Corresponding Author: Lili He. Email: email.

Computers, Materials & Continua 2020, 64(1), 207-216. https://doi.org/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 algorithms were chosen for comparison of learning performance: Constrained-K-Means, Seeded-K-Means, SAP (Semi-supervised Affinity Propagation), LSAP (Loose Seed AP) and CSAP (Compact Seed AP). In order to adapt the single label, this paper improves the above algorithms to SCKM (improved Constrained-K-Means), SSKM (improved Seeded-K-Means), and SSAP (improved Semi-supervised Affinity Propagationg) to perform semi-supervised clustering analysis on the data. A DSAP (Double Seed AP) semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering effect. The unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.

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APA Style
Jiang, Y., Yu, D., Zhao, M., Bai, H., Wang, C. et al. (2020). Analysis of semi-supervised text clustering algorithm on marine data. Computers, Materials & Continua, 64(1), 207-216. https://doi.org/10.32604/cmc.2020.09861
Vancouver Style
Jiang Y, Yu D, Zhao M, Bai H, Wang C, He L. Analysis of semi-supervised text clustering algorithm on marine data. Comput Mater Contin. 2020;64(1):207-216 https://doi.org/10.32604/cmc.2020.09861
IEEE Style
Y. Jiang, D. Yu, M. Zhao, H. Bai, C. Wang, and L. He "Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data," Comput. Mater. Contin., vol. 64, no. 1, pp. 207-216. 2020. https://doi.org/10.32604/cmc.2020.09861

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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