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Clustering with -regular graphs
Pattern Recognition, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jong Kyoung Kim, Seungjin Choi
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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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IEEE Transactions on Pattern Analysis and Machine Intelligence
Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations ...
Man-Sheng Chen +4 more
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Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations ...
Man-Sheng Chen +4 more
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Clustering with neighborhood graphs
2010Graph clustering methods are defined for general weighted graphs. If data is given in the form of points and distances between them, a neighborhood graph, such as the r-graph or kNN-graphs, is constructed and graph clustering is applied to this graph.
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Visualizing Graphs and Clusters as Maps
IEEE Computer Graphics and Applications, 2010Information visualization is essential in making sense of large datasets. Often, high-dimensional data are visualized as a collection of points in 2D space through dimensionality reduction techniques. However, these traditional methods often don't capture the underlying structural information, clustering, and neighborhoods well.
Yifan Hu 0001 +2 more
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Discrete Multi-Graph Clustering
IEEE Transactions on Image Processing, 2019Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfortunately, directly optimizing the spectral clustering inevitably results in an NP-hard problem due to the discrete constraints on the clustering labels ...
Minnan Luo +5 more
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Ortholog Clustering on a Multipartite Graph
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2005We present a method for automatically extracting groups of orthologous genes from a large set of genomes by a new clustering algorithm on a weighted multipartite graph. The method assigns a score to an arbitrary subset of genes from multiple genomes to assess the orthologous relationships between genes in the subset.
Akshay Vashist +2 more
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Adaptive Graph Auto-Encoder for General Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Xuelong Li, Hongyuan Zhang, Rui Zhang
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GMC: Graph-Based Multi-View Clustering
IEEE Transactions on Knowledge and Data Engineering, 2020Hao Wang, Yan Yang
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