A Faster k-means++ Algorithm

11/28/2022
by   Jiehao Liang, et al.
0

K-means++ is an important algorithm to choose initial cluster centers for the k-means clustering algorithm. In this work, we present a new algorithm that can solve the k-means++ problem with near optimal running time. Given n data points in ℝ^d, the current state-of-the-art algorithm runs in O(k ) iterations, and each iteration takes O(nd k) time. The overall running time is thus O(n d k^2). We propose a new algorithm FastKmeans++ that only takes in O(nd + nk^2) time, in total.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/24/2019

Circle Graph Isomorphism in Almost Linear Time

Circle graphs are intersection graphs of chords of a circle. In this pap...
research
07/30/2019

Parallelization of Kmeans++ using CUDA

K-means++ is an algorithm which is invented to improve the process of fi...
research
01/25/2017

Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D

The k-Means clustering problem on n points is NP-Hard for any dimension ...
research
01/31/2022

Fast Distributed k-Means with a Small Number of Rounds

We propose a new algorithm for k-means clustering in a distributed setti...
research
06/28/2020

Breathing k-Means

We propose a new algorithm for the k-means problem which repeatedly incr...
research
08/16/2023

A Quantum Approximation Scheme for k-Means

We give a quantum approximation scheme (i.e., (1 + ε)-approximation for ...
research
08/18/2023

Do you know what q-means?

Clustering is one of the most important tools for analysis of large data...

Please sign up or login with your details

Forgot password? Click here to reset