Results 231 to 240 of about 64,517 (265)
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Spectral Cluster Maps Versus Spectral Clustering
2020The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and kernel clustering methods.
Slawomir T. Wierzchon +1 more
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2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016
Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo +2 more
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Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo +2 more
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Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu 0001 +4 more
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Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu 0001 +4 more
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2003
In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties.
Bin Luo 0001 +2 more
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In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties.
Bin Luo 0001 +2 more
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Semidefinite spectral clustering
Pattern Recognition, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jaehwan Kim, Seungjin Choi
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Compressed Spectral Clustering
2009 IEEE International Conference on Data Mining Workshops, 2009Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible.
Bin Zhao 0004, Changshui Zhang
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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009
Recognizing and clustering similar faces are important for organizing digital photos. We present a novel clustering method based on spectral clustering to group faces in a photo album. The main contribution is the proposal of a distance metric that is robust to outlier features present in the facial images.
Biswaroop Palit +3 more
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Recognizing and clustering similar faces are important for organizing digital photos. We present a novel clustering method based on spectral clustering to group faces in a photo album. The main contribution is the proposal of a distance metric that is robust to outlier features present in the facial images.
Biswaroop Palit +3 more
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On clusterings-good, bad and spectral
Proceedings 41st Annual Symposium on Foundations of Computer Science, 2002We motivate and develop a natural bicriteria measure for assessing the quality of a clustering that avoids the drawbacks of existing measures. A simple recursive heuristic is shown to have poly-logarithmic worst-case guarantees under the new measure. The main result of the article is the analysis of a popular spectral
Ravi Kannan +2 more
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2008
In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
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In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
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Approximate Spectral Clustering
2009While spectral clustering has recently shown great promise, computational cost makes it infeasible for use with large data sets. To address this computational challenge, this paper considers the problem of approximate spectral clustering, which enables both the feasibility (of approximately clustering in very large and unloadable data sets) and ...
Liang Wang 0001 +3 more
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