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

Autor:	Blömer, Johannes, Lammersen, Christiane, Schmidt, Melanie, Sohler, Christian
Rok vydání:	2016
Předmět:	Computer Science - Data Structures and Algorithms Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1602.08254 Zobrazit plný text záznamu View this record from Arxiv