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

IF 2.3 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2011-12-01

Haizhou Wang, Mingzhou Song

引用次数: 0

Abstract

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.

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Ckmeans.1d.dp:动态规划的一维最优k均值聚类。

启发式k-均值算法被广泛用于聚类分析，但不能保证最优性。我们开发了一种用于最优一维聚类的动态规划算法。该算法被实现为一个名为Ckmeans.1d.dp的R包。我们证明了它在最优性和运行时间方面优于标准迭代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。

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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.

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