Journal Article10.1109/TKDE.2009.40
Clustering with Local and Global Regularization
Fei Wang,Changshui Zhang,Tao Li +2 more
TL;DR: Wang et al. as discussed by the authors proposed a clustering with local and global regularization (CLGR) method, which aims to minimize a cost function that properly trades off the local cost and global cost.
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Abstract: Clustering is an old research topic in data mining and machine learning. Most of the traditional clustering methods can be categorized as local or global ones. In this paper, a novel clustering method that can explore both the local and global information in the data set is proposed. The method, Clustering with Local and Global Regularization (CLGR), aims to minimize a cost function that properly trades off the local and global costs. We show that such an optimization problem can be solved by the eigenvalue decomposition of a sparse symmetric matrix, which can be done efficiently using iterative methods. Finally, the experimental results on several data sets are presented to show the effectiveness of our method.
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Citations
Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering
TL;DR: This paper proposes the spectral embedded clustering (SEC) framework, in which a linearity regularization is explicitly added into the objective function of SC methods, and presents a new Laplacian matrix constructed from a local regression of each pattern to capture both local and global discriminative information for clustering.
385
Structured graph learning for clustering and semi-supervised classification
TL;DR: This paper proposes a graph learning framework to preserve both the local and global structure of data that uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure.
Robust graph regularized nonnegative matrix factorization for clustering
TL;DR: This paper presents a novel robust graph regularized NMF model (RGNMF) to approximate the data matrix for clustering and shows that the proposed method consistently outperforms many state-of-the-art methods.
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Semi-supervised fuzzy clustering with metric learning and entropy regularization
Xuesong Yin,Ting Shu,Qi Huang +2 more
TL;DR: This work develops a novel semi-supervised metric-based fuzzy clustering algorithm called SMUC, which focuses on learning a Mahalanobis distance metric from side information given by the user to displace the Euclidean distance in FCM-based methods.
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Context-Aware Hypergraph Construction for Robust Spectral Clustering
TL;DR: Zhang et al. as discussed by the authors proposed a context-aware hypergraph similarity measure (CAHSM), which leads to robust spectral clustering in the case of noisy data, and constructed three types of hypergraphs-the pairwise hypergraph, the k-nearest-neighbor (kNN), and the high-order over-clustering hypergraph.
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