Distributed Clustering based on Distributional Kernel

• URL: http://arxiv.org/abs/2409.09418v1
• Date: Sat, 14 Sep 2024 11:40:54 GMT
• Title: Distributed Clustering based on Distributional Kernel
• Authors: Hang Zhang, Yang Xu, Lei Gong, Ye Zhu, Kai Ming Ting,
• Abstract summary: This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC. KDC guarantees that the combined clustering outcome from all sites is equivalent to the clustering outcome of its centralized counterpart from the combined dataset from all sites. The distribution-based clustering leads directly to significantly better clustering outcomes than existing methods of distributed clustering.
• Score: 14.797889234277978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final clusters based on the similarity with respect to the distributions of initial clusters, as measured by K. It is the only framework that satisfies all three of the following properties. First, KDC guarantees that the combined clustering outcome from all sites is equivalent to the clustering outcome of its centralized counterpart from the combined dataset from all sites. Second, the maximum runtime cost of any site in distributed mode is smaller than the runtime cost in centralized mode. Third, it is designed to discover clusters of arbitrary shapes, sizes and densities. To the best of our knowledge, this is the first distributed clustering framework that employs a distributional kernel. The distribution-based clustering leads directly to significantly better clustering outcomes than existing methods of distributed clustering. In addition, we introduce a new clustering algorithm called Kernel Bounded Cluster Cores, which is the best clustering algorithm applied to KDC among existing clustering algorithms. We also show that KDC is a generic framework that enables a quadratic time clustering algorithm to deal with large datasets that would otherwise be impossible.

Related papers

• Clustering Based on Density Propagation and Subcluster Merging [92.15924057172195]
  We propose a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation process.
  arXiv  Detail & Related papers  (2024-11-04T04:09:36Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters [2.4953699842881605]
  We focus on the concept of unimodality and propose a flexible cluster definition called locally unimodal cluster. A locally unimodal cluster extends for as long as unimodality is locally preserved across pairs of subclusters of the data. We propose the UniForCE method for locally unimodal clustering.
  arXiv  Detail & Related papers  (2023-12-18T16:19:02Z)
• Dynamically Weighted Federated k-Means [0.0]
  Federated clustering enables multiple data sources to collaboratively cluster their data, maintaining decentralization and preserving privacy. We introduce a novel federated clustering algorithm named Dynamically Weighted Federated k-means (DWF k-means) based on Lloyd's method for k-means clustering. We conduct experiments on multiple datasets and data distribution settings to evaluate the performance of our algorithm in terms of clustering score, accuracy, and v-measure.
  arXiv  Detail & Related papers  (2023-10-23T12:28:21Z)
• Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
  We propose a new deep graph clustering method termed Reinforcement Graph Clustering. In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
  arXiv  Detail & Related papers  (2023-08-13T18:12:28Z)
• Socially Fair Center-based and Linear Subspace Clustering [8.355270405285909]
  Center-based clustering and linear subspace clustering are popular techniques to partition real-world data into smaller clusters. Different clustering cost per point for different sensitive groups can lead to fairness-related harms. We propose a unified framework to solve socially fair center-based clustering and linear subspace clustering.
  arXiv  Detail & Related papers  (2022-08-22T07:10:17Z)
• Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph [73.68184322526338]
  Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. This paper proposes a novel local sample-weighted multiple kernel clustering model. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms.
  arXiv  Detail & Related papers  (2022-07-05T05:00:38Z)
• DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks [53.88811980967342]
  This paper presents a Deep Clustering via Ensembles (DeepCluE) approach. It bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
  arXiv  Detail & Related papers  (2022-06-01T09:51:38Z)
• Very Compact Clusters with Structural Regularization via Similarity and Connectivity [3.779514860341336]
  We propose an end-to-end deep clustering algorithm, i.e., Very Compact Clusters (VCC) for the general datasets. Our proposed approach achieves better clustering performance over most of the state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2021-06-09T23:22:03Z)
• 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)
• Point-Set Kernel Clustering [11.093960688450602]
  This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects. We show that the new clustering procedure is both effective and efficient that enables it to deal with large scale datasets.
  arXiv  Detail & Related papers  (2020-02-14T00:00:03Z)

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.