4.7 Review

A review on semi-supervised clustering

Related references

Note: Only part of the references are listed.
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Summary: Semi-supervised clustering aims to utilize prior knowledge to improve clustering performance. Existing methods do not adequately consider the natural gap between class information and clustering when using partial labeling information. In order to address this issue, a compact-cluster assumption is proposed along with a general framework called CSSC, which supervises traditional clustering using an objective function that measures the compactness of clusters. Two effective solutions for Kmeans and spectral clustering are provided within this framework. The proposed method is shown to be feasible and effective through theoretical analyses and extensive experiments on real-world datasets.

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Summary: Hierarchical agglomerative methods are effective and popular for clustering data, but have not been systematically compared regarding false positives when searching for clusters. A cluster model involving a higher density nucleus, transition, and outliers is used to quantify the relevance of obtained clusters and address false positive issues. Experiment results show many methods detecting two clusters in unimodal data, with single-linkage method being more resilient to false positives.

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A New semi-supervised clustering for incomplete data

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Xiangli Li et al.

Summary: Nonnegative matrix factorization (NMF) is an effective method for high dimensional data analysis, but it cannot utilize label information. To address this, a semi-supervised sparse neighbor constrained co-clustering model (SSCCDS) is proposed. By introducing co-clustering and regularization constraints, SSCCDS overcomes the limitations of traditional NMF and achieves good clustering performance, as demonstrated by experiments on different datasets.

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Summary: In this paper, a new clustering algorithm named ISBFK-means based on the influence space is proposed to address the issues of huge time overhead and unstable clustering quality when running the K-means algorithm on massive raw data. The approach effectively reduces data volume in the clustering process and improves the stability of clustering quality. Experimental results demonstrate the algorithm's high performance in processing celestial spectral data.

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Multiview clustering via exclusive non-negative subspace learning and constraint propagation

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Confidence-weighted safe semi-supervised clustering

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Semi-supervised clustering of unknown expressions

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Semi-supervised multi-view clustering based on constrained nonnegative matrix factorization

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Semi-Supervised Ensemble Clustering Based on Selected Constraint Projection

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A semi-supervised approximate spectral clustering algorithm based on HMRF model

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Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering

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Di Wang et al.

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Large-scale spectral clustering based on pairwise constraints

T. Semertzidis et al.

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Semi-supervised evolutionary ensembles for Web video categorization

Amjad Mahmood et al.

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A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization

Liang Yang et al.

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Semi-supervised clustering for MR brain image segmentation

Nara M. Portela et al.

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Active Learning of Constraints for Semi-Supervised Clustering

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Jun Yu et al.

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Group extraction from professional social network using a new semi-supervised hierarchical clustering

Eya Ben Ahmed et al.

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Exhaustive and Efficient Constraint Propagation: A Graph-Based Learning Approach and Its Applications

Zhiwu Lu et al.

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Efficient Semisupervised MEDLINE Document Clustering With MeSH-Semantic and Global-Content Constraints

Jun Gu et al.

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Constrained Nonnegative Matrix Factorization for Image Representation

Haifeng Liu et al.

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Haibin Zhu et al.

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Xuesong Yin et al.

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Spectral clustering: A semi-supervised approach

Weifu Chen et al.

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A semi-supervised fuzzy clustering algorithm applied to gene expression data

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Graph Regularized Nonnegative Matrix Factorization for Data Representation

Deng Cai et al.

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Density-based semi-supervised clustering

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Integrating Clustering and Supervised Learning for Categorical Data Analysis

Ujjwal Maulik et al.

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Ian Davidson et al.

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Semi-supervised fuzzy clustering: A kernel-based approach

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Active semi-supervised fuzzy clustering

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Hong Chang et al.

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