Work

Theoretical Analysis of the $k$-Means Algorithm - A Survey

Year: 2016

Type: preprint

Abstract: The $k$-means algorithm is one of the most widely used clustering heuristics. Despite its simplicity, analyzing its running time and quality of approximation is surprisingly difficult and can lead to deep insights that can be used to improve the algorithm. In this paper we survey the recent results in this direction as well as several extension of the basic $k$-means method.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1602.08254

Authors: Blömer, Johannes, Lammersen, Christiane, Schmidt, Melanie, Sohler, Christian

Institutions: Paderborn University, Simon Fraser University, Carnegie Mellon University, TU Dortmund University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2950379879

Cites: 62

Cited by: 9

Related to: 20