A Comprehensive Survey on Spectral Clustering with Graph Structure Learning

• URL: http://arxiv.org/abs/2501.13597v2
• Date: Fri, 24 Jan 2025 10:40:13 GMT
• Title: A Comprehensive Survey on Spectral Clustering with Graph Structure Learning
• Authors: Kamal Berahmand, Farid Saberi-Movahed, Razieh Sheikhpour, Yuefeng Li, Mahdi Jalili,
• Abstract summary: Spectral clustering is a powerful technique for clustering high-dimensional data. We explore various graph clustering techniques, including pairwise, anchor, and hypergraph-based methods. We discuss multi-view clustering frameworks, examining their applications within one-step and two-step clustering processes.
• Score: 10.579153358536372
• License:
• Abstract: Spectral clustering is a powerful technique for clustering high-dimensional data, utilizing graph-based representations to detect complex, non-linear structures and non-convex clusters. The construction of a similarity graph is essential for ensuring accurate and effective clustering, making graph structure learning (GSL) central for enhancing spectral clustering performance in response to the growing demand for scalable solutions. Despite advancements in GSL, there is a lack of comprehensive surveys specifically addressing its role within spectral clustering. To bridge this gap, this survey presents a comprehensive review of spectral clustering methods, emphasizing on the critical role of GSL. We explore various graph construction techniques, including pairwise, anchor, and hypergraph-based methods, in both fixed and adaptive settings. Additionally, we categorize spectral clustering approaches into single-view and multi-view frameworks, examining their applications within one-step and two-step clustering processes. We also discuss multi-view information fusion techniques and their impact on clustering data. By addressing current challenges and proposing future research directions, this survey provides valuable insights for advancing spectral clustering methodologies and highlights the pivotal role of GSL in tackling large-scale and high-dimensional data clustering tasks.

Related papers

• Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective [52.662463893268225]
  Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. Existing SHGL methods encounter two significant limitations. We introduce a novel framework enhanced by rank and dual consistency constraints.
  arXiv  Detail & Related papers  (2024-12-01T09:33:20Z)
• Spectral Clustering in Convex and Constrained Settings [0.0]
  We introduce a novel framework for seamlessly integrating pairwise constraints into semidefinite spectral clustering. Our methodology systematically extends the capabilities of semidefinite spectral clustering to capture complex data structures.
  arXiv  Detail & Related papers  (2024-04-03T18:50:14Z)
• Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering [2.44755919161855]
  Spectral clustering is a popular method for effectively clustering nonlinearly separable data. This paper introduces a novel approach called parametric spectral clustering (PSC) PSC addresses the challenges associated with big data and real-time scenarios.
  arXiv  Detail & Related papers  (2023-11-14T01:26:20Z)
• Efficient Multi-View Graph Clustering with Local and Global Structure Preservation [59.49018175496533]
  We propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG) Specifically, EMVGC-LG jointly optimize anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number.
  arXiv  Detail & Related papers  (2023-08-31T12:12:30Z)
• FedSpectral+: Spectral Clustering using Federated Learning [0.0]
  We propose a spectral clustering algorithm using federated learning (FL) to overcome issues of data privacy and increased communication costs. FL is a privacy-protecting algorithm that accumulates model parameters from each local learner rather than collecting users' raw data. FedSpectral is a baseline approach that uses local spectral clustering labels to aggregate the global spectral clustering by creating a similarity graph. FedSpectral+, a state-of-the-art approach, uses the power method to learn the global spectral embedding by incorporating the entire graph data without access to the raw information distributed among the clients.
  arXiv  Detail & Related papers  (2023-02-04T09:28:36Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• GLCC: A General Framework for Graph-level Clustering [5.069852282550117]
  This paper studies the problem of graph-level clustering, which is a novel yet challenging task. We propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) Experiments on a range of well-known datasets demonstrate the superiority of our proposed GLCC over competitive baselines.
  arXiv  Detail & Related papers  (2022-10-21T11:08:10Z)
• Deep Clustering: A Comprehensive Survey [53.387957674512585]
  Clustering analysis plays an indispensable role in machine learning and data mining. Deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering.
  arXiv  Detail & Related papers  (2022-10-09T02:31:32Z)
• Enhancing cluster analysis via topological manifold learning [0.3823356975862006]
  We show that inferring the topological structure of a dataset before clustering can considerably enhance cluster detection. We combine manifold learning method UMAP for inferring the topological structure with density-based clustering method DBSCAN.
  arXiv  Detail & Related papers  (2022-07-01T15:53:39Z)
• Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image [88.60285937702304]
  This paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The proposed SSCAG is competitive against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-24T08:09:27Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.