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资源简介：
Patent clustering using spectral clustering.
应用场景：
创建时间：
2020-02-05
相关数据集
BA (Binary Alphabet)
集成聚类因其在聚类任务中的高性能而在机器学习和数据挖掘中引起了广泛关注。谱聚类是最流行的聚类方法之一，与传统的聚类方法相比具有优越的性能。现有的集成聚类方法通常直接使用基聚类算法的聚类结果进行集成学习，不能很好地利用图拉普拉斯算子在谱聚类中探索到的内在数据结构，从而无法获得理想的聚类结果。在本文中，我们提出了一种基于谱聚类的聚类算法的新集成学习方法。所提出的方法不是直接使用从每个基本谱聚类算法获得
OpenDataLab2026-07-12 更新180
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenges to the eigenvalue decomposition therein. I
DataCite Commons2023-03-29 更新100
Improving Spectral Clustering Using the Asymptotic Value of the Normalized Cut
Spectral clustering (SC) is a popular and versatile clustering method based on a relaxation of the normalized graph cut objective. Despite its popularity, selecting the number of clusters and tuning t
DataCite Commons2021-09-29 更新90
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenges to the eigenvalue decomposition therein. I
DataCite Commons2023-03-29 更新130
Additional file 5 of SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering
Additional file 5: Table S2. Experimental Data sources. Genome annotation (hg19/GRCh37) [50] data for GM12878 cell line used in the analysis, sorted by category, then by data type.
DataCite Commons2020-08-25 更新90
