Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (4): 24-34.doi: 10.6040/j.issn.1672-3961.0.2020.398

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Adaptive semi-supervised neighborhood clustering algorithm

ZHU Hengdong1, MA Yingcang1*, DAI Xuezhen2   

  1. 1. School of Science, Xi'an Polytechnic University, Xi'an 710600, Shaanxi, China;
    2. Xi'an Traffic Engineering College, Xi'an 710300, Shaanxi, China
  • Published:2021-08-18

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Abstract: In order to make full use of the supervision information to guide the clustering process, an adaptive semi-supervised neighborhood clustering algorithm(SSCAN)was proposed. The combination of supervision matrix and distance measurement was introduced to construct a reasonable similarity matrix; The supervision information was fully utilized to adjust the model through the combination of the label information matrix and the manifold regular term to improve the clustering effect. Through experiments on various data sets and comparison with other clustering algorithms, the results showed that the SSCAN algorithm could make full use of the supervision information and improve the accuracy of clustering.

Key words: semi-supervised learning, manifold regular term, label information, clustering, distance measurement

CLC Number: 

  • TP181
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