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
[1] JAIN A K, MURTY M N, FLYNN P J. Data clustering: a review[J]. Acm Computing Surveys, 1999, 31(3): 264-323.
[2] 刘友超,张曦煌. 基于密度自适应邻域相似图的半监督谱聚类[J]. 计算机应用研究, 2020, 37(9): 2604-2609. LIU Youchao, ZHANG Xihuang. Semi-supervised spectral clustering based on density adaptive neighborhood similarity graphs [J]. Computer Application Research, 2020, 37(9):2604-2609.
[3] 赵佳, 王士同. 特征加权距离的半监督模糊子空间聚类算法[J]. 小型微型计算机系统, 2017, 38(2): 405-410. ZHAO Jia, WANG Shitong. Semi-supervised fuzzy subspace clustering algorithm based on feature weighted distance[J]. Small Microcomputer System, 2017, 38(2): 405-410.
[4] ZHUANG L, ZHOU Z, GAO S, et al. Label information guided graph construction for semi-supervised learning[J]. IEEE Transactions on Image Processing, 2017: 4182-4192.
[5] QIU S, NIE F, XU X, et al. Accelerating flexible manifold embedding for scalable semi-supervised learning[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2018: 2286-2295.
[6] WAGSTAFF K, CLAIRE C. Clustering with instance level constraints[C] //Proceedings of 17th International Conference on Machine Learning. San Francisco,USA: MKPI Press, 2000: 1097-1103.
[7] JI X, XU W. Document clustering with prior knowledge[C] //Proceedings of the 29th Annual International ACM SIGIR Conferenceon Research and Development in Information Retrieval. Seattle, USA: DBLP Press, 2006: 405-412.
[8] SHENTAL N, BAR-HILLEL A, HERTZ T, et al. Gaussian mixture models with equivalence constraints[C] //Proceedings of Neural Information Processing Systems. Vancouver, Canada: NIPS Press, 2009: 33-58.
[9] LU Z, LEEN T K. Semi-supervised learning with penalized probabilistic clustering[C] //Proceedings of Neural Information Processing Systems. Vancouver, Canada: DBLP Press, 2004: 849-856.
[10] COZMAN F G, COHEN I. Unlabeled data can degrade classification performance of generative classifiers[C] //Proceedings of the 15th International Florida Artificial Intelligence Research Society Conference. Florida, USA: DBLP Press, 2002: 327-331.
[11] ZHOU D, BOUSQUET O, LAL T N, et al. Learning with local and global consistency[J]. Advances in Neural Information Processing Systems, 2004, 16(3): 321-328.
[12] XIANG L, YAO W, ESTER M, et al. Semi-supervised clustering in attributed heterogeneous information networks[C] // Proceedings of the 26th International Conference. Perth, Australia: IEEE Press, 2017: 1621-1629.
[13] BASU S, BANERJEE A, MOONEY R. Semi-supervised clustering by seeding[C] // Proceedings of 19th International Conference on Machine Learning. Sydney, Australia: DBLP Press, 2002: 27-34.
[14] GAO J, TAN P N, CHENG H. Semi-supervised clustering with partial background information[C] //Pro-ceedings of the 6th SIAM International Conference on Data Mining. Maryland, USA: DBLP Press, 2006: 489-493.
[15] ZHOU D, SCHOLKOPF B. A regularization framework for learning from graph data[C] //Proceedings of the Workshop on Statistical Relational Learning at 21st International Conference on Machine Learning. Alberta, Canada: ICML Press, 2004: 132-137.
[16] JIN X, LIU S Y, HAO D. Distributed semi-supervised learning algorithm based on extreme learning machine over networks using event-triggered communication scheme[J]. Neural Networks, 2019, 119: 261-272.
[17] KANNO Y, KANEKO H. Improvement of predictive accuracy in semi-supervised regression analysis by selecting unlabeled chemical structures[J]. Chemometrics and Intelligent Laboratory Systems, 2019, 191: 82-87.
[18] BAI Y, LIU S, YIN K, et al. Variational community partition with novel network structure centrality prior[J]. Applied Mathematical Modelling, 2019, 75: 333-348.
[19] WANG J, SHAO W, SONG Z. Semi-supervised variational bayesian student's t mixture regression and robust inferential sensor application[J]. Control Engineering Practice, 2019, 92:104155.1-104155.15.
[20] NIE F, WANG X, HUANG H. Clustering and projected clustering with adaptive neighbors[C] //Proceedings of the 29th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2014: 977-986.
[21] NIE F, WANG C L, LI X. K-multiple-means: a multiple-means clustering method with specified k clusters[C] //Proceedings of the 25th ACM SIGKDD International Conference. Anchorage, USA: ACM Press, 2019: 959-967.
[22] NIE F, WANG X, JORDAN M I, et al. The constrained laplacian rank algorithm for graph-based clustering[C] //Proceedings of the 30th AAAI Conference on Artificial Intelligence. Arizona, USA: AAAI Press, 2016: 1969-1976.
[23] HAO C D, YU F L, ZHOU Z H. Learning from semi-supervised weak-label data[C] //Proceedings of the 32th AAAI Conference on Artificial Intelligence. Louisiana, USA: DBLP Press, 2018: 2926-2933.
[24] NIE F, WANG H, HUANG H, et al. Adaptive loss minimization for semi-supervised elastic embedding[C] //Proceedings of the 23th International Joint Conference on Artificial Intelligence. Beijing, China: AAAI Press, 2013: 1565-1571.
[25] KULIS B, BASU S, DHILLON I, et al. Semi-supervised graph clustering: a kernel approach[J]. Machine Learning, 2009, 74(1): 1-22.
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