Ckmeans.1d。动态规划的一维最优k-均值聚类

IF 2.3 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal Pub Date : 2011-12-01 DOI:10.32614/RJ-2011-015

Haizhou Wang, Mingzhou Song

引用次数: 332

摘要

广泛用于聚类分析的启发式k-均值算法不能保证最优性。我们开发了一种动态规划算法来优化一维聚类。该算法是作为一个名为Ckmeans.1d.dp的R包实现的。我们证明了它在最优性和运行时间上优于标准迭代k-means算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming

The heuristic k-means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp. We demonstrate its advantage in optimality and runtime over the standard iterative k-means algorithm.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.