Results 31 to 40 of about 178,314 (358)
Improved Graph Clustering [PDF]
IEEE Transactions on Information Theory, 2014 Graph clustering involves the task of dividing nodes into clusters, so that the edge density is higher within clusters as opposed to across clusters. A natural, classic and popular statistical setting for evaluating solutions to this problem is the stochastic block model, also referred to as the planted partition model.
Yudong Chen 0001 +2 more
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Multi-view Clustering Based on Bipartite Graph Cross-view Graph Diffusion [PDF]
Jisuanji kexueMulti-view clustering is an research hotspots in the field of unsupervised learning.Recently,the method based on cross-view graph diffusion uses the complementary information between multiple views to obtain a unified graph for clustering on the basis of
WANG Jinfu, WANG Siwei, LIANG Weixuan, YU Shengju, ZHU En
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Graph-based data clustering via multiscale community detection
Applied Network Science, 2020 We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation
Zijing Liu, Mauricio Barahona
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SACOC: A spectral-based ACO clustering algorithm [PDF]
, 2014 The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning
Otero, Fernando E. B. +7 more
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Ensemble Clustering for Graphs [PDF]
, 2018 9 pages, 5 ...
Valérie Poulin, François Théberge
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Adaptive Graph Representation for Clustering
IEEE Access, 2022 Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen +5 more
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四川大学学报. 自然科学版Clustering is an important unsupervised learning technique in machine learning and artificial intelligence.Clustering algorithms can uncover latent structures and patterns from unlabeled data， and provide strong support for subsequent data analysis ...
GAO Hai-Yan, HE Wen-Hui
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MACOC: a medoid-based ACO clustering algorithm [PDF]
, 2014 The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning
Otero, Fernando E. B. +7 more
core +1 more source
Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection
Entropy, 2023 In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect ...
Ni Li, Manman Peng, Qiang Wu
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CoRR, 2018 It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions.
Sonja Biedermann +3 more
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