k-均值聚类在高维大数据上的高效算法研究进展

doi:10.11896/jsjkx.251000037

Abstract

Abstract: Clustering is a classic task in machine learning.The goal of clustering is to partition data points into groups,with respect to a similarity measure.As one of the most fundamental models for clustering,k-means has been extensively studied and widely applied.This paper focuses on the computational issue of solving k-means efficiently,and discusses the progress of(near-) linear time approximation algorithms for k-means,from the perspective of theoretical computer science.It also briefly discusses the status of clustering algorithms in various big data computational models,including dynamic,streaming and distributed computing.

Key words: k-means, Euclidean spaces, Near-linear time algorithms, Sublinear algorithms

CLC Number:

TP18

GAO Guichen, JIANG Shaofeng. Recent Advances in Efficient Algorithms for k-Means Clustering on High-dimensional Big Data[J].Computer Science, 2026, 53(4): 24-32.

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