Publications related to Lloyd's algorithm

Improving K-means Clustering Using Speculation

Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou, Stefan Igescu

K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

2023

A finite element framework based on bivariate simplex splines on triangle configurations

Xiaodong Wei, Juan Cao

Recently, triangle configuration based bivariate simplex splines (referred to as TCB-spline) have been introduced to the geometric computing community. TCB-splines retain many attractive theoretic properties of classical B-splines, such as partition of uni ...

ELSEVIER SCIENCE SA2019

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics

Jian Li, Lingxiao Huang

We study the problem of constructing epsilon-coresets for the (k, z)-clustering problem in a doubling metric M(X, d). An epsilon-coreset is a weighted subset S subset of X with weight function w : S -> R->= 0, such that for any k-subset C is an element of ...

IEEE COMPUTER SOC2018

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Sebastian Urban Stich

We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particular, our algorithm can be specialized to invert positive definite matrices i ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Superpixels and Polygons using Simple Non-Iterative Clustering

Sabine Süsstrunk, Radhakrishna Achanta

We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel bou ...

Ieee2017

Data-driven spatio-temporal discretization for pedestrian flow characterization

Michel Bierlaire, Marija Nikolic

We propose a novel approach to pedestrian flow characterization. The definitions of density, flow and velocity existing in the literature are extended through a data-driven spatio-temporal discretization framework. The framework is based on three-dimension ...

Elsevier Science Bv2017

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Nikolaos Geroliminis, Mohammadreza Saeedmanesh

Aggregated network level modeling and control of traffic in urban networks have recently gained a lot of interest due to unpredictability of travel behaviors and high complexity of physical modeling in microscopic level. Recent research has shown the exist ...

Elsevier2016

Load Balancing and Skew Resilience for Parallel Joins

Christoph Koch, Aleksandar Vitorovic

We address the problem of load balancing for parallel joins. We show that the distribution of input data received and the output data produced by worker machines are both important for performance. As a result, previous work, which optimizes either for inp ...

2016

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Lloyd's algorithm

Improving K-means Clustering Using Speculation

A finite element framework based on bivariate simplex splines on triangle configurations

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Superpixels and Polygons using Simple Non-Iterative Clustering

Data-driven spatio-temporal discretization for pedestrian flow characterization

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Load Balancing and Skew Resilience for Parallel Joins

Automated tracking of temporal displacements of a red blood cell obtained by time-lapse digital holographic microscopy

Graph Chatbot

Chat with Graph Search

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Superpixels and Polygons using Simple Non-Iterative Clustering

Data-driven spatio-temporal discretization for pedestrian flow characterization

Automated tracking of temporal displacements of a red blood cell obtained by time-lapse digital holographic microscopy

Load Balancing and Skew Resilience for Parallel Joins

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Improving K-means Clustering Using Speculation

A finite element framework based on bivariate simplex splines on triangle configurations

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics