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Improved semi-supervised clustering Algorithm Based on Affinity Propagation

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Journal of Donghua University(English Edition) 2015年第1期32卷 125-131页

作者：金冉刘瑞娟李晔锋寇春海 College of Information Science and Technology Donghua University College of Computer Science and Technology Zhejiang Wanli University College of Science Donghua University

A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering( APC) process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination( SAP-LC algorithm) have been greatly improved.

关键词： semi-supervised clustering affinity propagation(AP) layered combination computation complexity combined promotion

A semi-supervised Text clustering Algorithm with Word Distribution Weights

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A Semi-Supervised Text Clustering Algorithm with Word Distri...

2013教育技术与信息系统国际会议

作者： Ping Zhou Jiayin Wei Yongbin Qin College of Computer Science and Information Guizhou University

semi-supervised text clustering, as a research branch of the text clustering, aims at employing limited priori knowledge to aid unsupervised text clustering process, and helping users get improved clustering results. Because labeled data are difficult, expensive and time-consuming to obtain, it is important to use the supervised information effectively to improve the performance of clustering significantly. This paper proposes a semi-supervised LDA text clustering algorithm based on the weights of word distribution (WWDLDA). By introducing the coefficients of word distribution obtained from labeled data, LDA model can be used in the field of semi-supervised clustering. In the process of clustering, coefficients always adjust the word distribution to change the clustering results. Our experimental results on real data sets show that the proposed semi-supervised text clustering algorithm can get better clustering results than constrained mixmnl, where mixmnl stands for multinomial model-based EM algorithm.

关键词： Text clustering semi-supervised clustering LDA Word Distribution

Active learning-based semi-supervised spectral clustering algorithm

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Active learning-based semi-supervised spectral clustering al...

2018中国信息通信大会(CICC2018)

作者： JIANG Wei-Jin CHEN Jia-Hui XU Yu-Hui School of Computer National University of Defense Technology School of computer science and technology Wuhan university Institute of Big Data and Internet Innovation Mobile E-business Collaborative Innovation Center of Hunan Province Hunan University of Commerce School of Computer Science and Technology Wuhan University of Technology North navigation control technology Co. Ltd.

semi-suppervised learning is one of the hottest research topics in the Machine *** performance of semi-supervised clustering depends on the quality of supervision information,so it is necessary to actively learn high quality supervision *** active learning algorithm based on pair-wise constraints with error correction is proposed in this *** algorithm searches the pair-wise constraints information which clustering algorithm can’t find,and try its best to reduce connections between these constraint informations,which is used in the spectral clustering,utilizing suppervised information adjust the distance matrix in the spectral clustering,and sort the *** algorithm makes the learninger can study actively when the learinger receives the data without flags by the two-way search method,and get better clustering result with less ***,the algorithm reduces the computational complexity of the semi-supervised algorithms based on constraints and resolves the singular problem of the pair-wise constraints in the clustering *** results on UCI benchmark data sets and artificial data set states clearly the performance of the algorithm is better than other compared algorithms,and the performance of algorithm is better than the ones of the spectral clustering which randomly selects the supervision information.

关键词： semi-supervised clustering active learning semi-supervised learning pairwise constraint spectral clustering

Discriminatively Constrained semi-supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

semi-supervised Artificial Immune Mixture Models clustering

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Chinese Journal of Electronics 2016年第2期25卷 249-255页

作者： HUANG Wenlong WANG Xiaodan LI Guohong KANG Hongxia Air and Missile Defense College Air Force Engineering University State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control Center

Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of an algorithm. The traditional Expectation maximization(EM) algorithm only produces locally optimal solutions, it is sensitive to initialization, and the number of components of mixture model must be known in advance. We propose a novel semi-supervised clustering algorithm that uses Gaussian mixture models(GMM) as the underlying clustering model. A novel adaptive global search mechanism is introduced into semi-supervised gaussian mixture model-based clustering, where the EM algorithm is incorporated with the ideas of an immune clonal selection technique. The new algorithm overcomes the various problems associated with the traditional EM algorithm. And it can improve the effectiveness in estimating the parameters and determining the optimal number of clusters automatically. The experimental results illustrate the proposed algorithm provides significantly better clustering results, when compared with other methods of incorporating equivalence constraints.

关键词： semi-supervised clustering Mixture model Immune clonal selection Globally optimal solution

Global Optimization for semi-supervised K-means

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Global Optimization for Semi-supervised K-means

2009 Asia-Pacific Conference on Information Processing

作者： Xue Sun 1 Kunlun Li 1 Rui Zhao 1 1. College of Electronic and Information Engineering Hebei University Baoding China Xikun Hu 2 2. Industral and Commercial College Hebei University Baoding China

So far most of the K-means algorithms use the number of the labeled data as the K value, but sometimes it doesn’t work well. In this paper, we propose a semi-supervised K-means algorithm based on the global optimization. It can select an appropriate number of clusters as the K value directly and plan a great amount of supervision data by using only a small amount of the labeled data. Combining the distribution characteristics of data sets and monitoring information in each cluster after clustering, we use the voting rule to guide the cluster labeling in the data sets. The experiments indicated that the global optimization algorithm for semi-supervised K-means is quite helpful to improve the K-means algorithm, it can effectively find the best data sets for K values and clustering center and enhancing the performance of clustering.

关键词： semi-supervised clustering Optimization algorithm K-means Threshold

Constrained Silhouette Based Evolutionary K-Means

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Constrained Silhouette Based Evolutionary K-Means

2013年中国智能自动化学术会议

作者： Zhenfeng He College of Mathematics and Computer Science Fuzhou University

Evolutionary K-Means(EKM) is a non-parametric approach proposed to improve K-Means *** EKM approaches are ineffective in deciding the correct cluster number of real *** paper uses instance-level constraints to solve this problem and presents a Constrained Silhouette(CS) based algorithm,namely *** CS is defined to combine constraints into the computation of Silhouette Information(SI).Updated from the Fast Evolutionary Algorithm for clustering algorithm(F-EAC),CS-EAC uses CS instead of SI to guide the genetic *** results suggest that CS-EAC is effective in both deciding the correct number of clusters and improving the accuracy of clustering for real datasets.

关键词： clustering semi-supervised clustering Genetic algorithm Silhouette information