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Single Learning

Spectral Clustering

Single View

  • Robust and efficient multi-way spectral clustering, [matlab]

  • ICML 2018, Fast Approximate Spectral Clustering for Dynamic Networks -Lionel Martin, Andreas Loukas, Pierre Vandergheynst, arXiv:1706

  • Efficient Kernel Selection via Spectral Analysis Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang (PDF | Details)

  • Beyond the Nystrom Approximation: Speeding up Spectral Clustering using Uniform Sampling and Weighted Kernel k-means

  • Guided Graph Spectral Embedding: Application to the C. elegans Connectome

    • M Petrović, TAW Bolton, MG Preti, R Liégeois
    • (arXiv:1812)

Implementation

  • A fast implementation of spectral clustering on GPU-CPU Platform
    • Jin, Yu and JaJa, Joseph F,
    • code

k-means Clustering

Single View

NIPS 2018

  • Differentially Private k-Means with Constant Multiplicative Error

  • Statistical and Computational Trade-Offs in Kernel k-Means

  • Query k-means Clustering and the Double Dixie Cup Problem

NIPS 2017

  • Sparse embedded k-means clustering

  • k-Medoids For k-Means Seeding

  • Clustering Stable Instances of Euclidean k-means

  • Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting k-means, and Local Search

NIPS 2016

  • Fast and Provably Good Seedings for k-Means

ICML 2016

  • k-variates++: more pluses in the k-means++

  • Fast k-means with accurate bounds

  • k-Means Clustering with Distributed Dimensions

  • Speeding up k-means by approximating Euclidean distances via block vectors

    • Thomas Bottesch, Thomas Bühler, Markus Kächele ; PMLR 48:2578-2586
    • (PDF]

Implementation

UTS print

  • Chameleon: hierarchical clustering using dynamic modeling

    • G. Karypis ; Eui-Hong Han ; V. Kumar

  • A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

    • M Ester, HP Kriegel, J Sander, X Xu
    • (KDD 96)

  • DBSCAN revisited: mis-claim, un-fixability, and approximation

  • Depth-first search and linear graph algorithms

  • Learning kernels for variants of normalized cuts: Convex relaxations and applications

    • L Mukherjee, V Singh, J Peng

  • Multiway spectral clustering: A margin-based perspective

    • Z Zhang, MI Jordan - Statistical Science, 2008

  • Compressive Spectral Clustering

  • Clustering with Bregman Divergences

  • On the Effectiveness of Laplacian Normalization for Graph semi-supervised Learning

  • Dimensionality Reduction for Spectral Clustering

  • Iterative Discovery of Multiple Alterative Clustering Views

  • Distributed and Provably Good Seedings for K-means in Constant Rounds

  • Speeding up k-means by approximating Euclidean distances via block vectors

  • Unifor Deviation Bounds for k-means clustering

  • spectral clustering based on the graph p-Laplacian

  • Spectral clustering based on learning similarity matrix

  • deep spectral clustering learning

  • Learning on Graph with Laplacian Regularization

  • A local algorithm for finding well-connected clusters

  • Graph clustering with dynamic embedding

  • A fast and high quality multilevel scheme for partitioning irregular graphs