2013/09/09 by Jing Qian, Venkatesh Saligrama, Qian, Jing +1 · 2 citations
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Algorithm #Artificial intelligence #Bayesian Methods and Mixture Models #Cluster analysis #Computer science #Cut #Data mining #Data set #FOS: Computer and information sciences #Face and Expression Recognition #Graph #Limit (mathematics) #Machine Learning (stat.ML) #Mathematics #Maximum cut #Minimum cut #Node (physics) #Pattern recognition (psychology) #Set (abstract data type) #Spectral clustering #Theoretical computer science #stat.ML
paper · pdf · doi:10.48550/arxiv.1309.2303
published in arXiv (Cornell University) (Cornell University) · 24 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:1302.5134
arxiv created 2013/09/09 · openalex publication_date 2013/09/09 · arxiv updated 2013/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present. We show that Ratio-Cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced data since they tend to emphasize cut sizes over cut values. We propose a graph partitioning problem that seeks minimum cut partitions under minimum size constraints on partitions to deal with imbalanced data. Our approach parameterizes a family of graphs, by adaptively modulating node degrees on a fixed node set, to yield a set of parameter dependent cuts reflecting varying levels of imbalance. The solution to our problem is then obtained by optimizing over these parameters. We present rigorous limit cut analysis results to justify our approach. We demonstrate the superiority of our method through unsupervised and semi-supervised experiments on synthetic and real data sets.