问题描述
我正在使用标准stats包:kmeans(dataset, centers = 100, nstart = 25, iter.max = 20)在具有636,688行和7列的数据集上的R中运行k-means聚类.
I am running k-means clustering in R on a dataset with 636,688 rows and 7 columns using the standard stats package: kmeans(dataset, centers = 100, nstart = 25, iter.max = 20).
我收到以下错误:Quick-TRANSfer stage steps exceeded maximum (= 31834400)，尽管可以在-我不确定出了什么问题.我认为我的问题与数据集的大小有关，但是如果有人可以一劳永逸地阐明我可以采取的缓解措施，我将不胜感激.
I get the following error: Quick-TRANSfer stage steps exceeded maximum (= 31834400), and although one can view the code at http://svn.r-project.org/R/trunk/src/library/stats/R/kmeans.R - I am unsure as to what is going wrong. I assume my problem has to do with the size of my dataset, but I would be grateful if someone could clarify once and for all what I can do to mitigate the issue.
推荐答案
我只是遇到了同样的问题.
I just had the same issue.
通过?kmeans参见R中kmeans的文档:
See the documentation of kmeans in R via ?kmeans:
在这种情况下，您可能需要切换到Lloyd或MacQueen算法.
In these cases, you may need to switch to the Lloyd or MacQueen algorithms.
这里关于R的讨厌的事情是它继续发出可能未被注意的警告.出于基准测试的目的，我认为这是一次失败的运行，因此我使用:
The nasty thing about R here is that it continues with a warning that may go unnoticed. For my benchmark purposes, I consider this to be a failed run, and thus I use:
if (kms$ifault==4) { stop("Failed in Quick-Transfer"); }
根据您的用例，您可能想要做类似的事情
Depending on your use case, you may want to do something like
if (kms$ifault==4) { kms = kmeans(X, kms$centers, algorithm="MacQueen"); }
相反，继续使用其他算法.
instead, to continue with a different algorithm.
如果要对K均值进行基准测试，请注意R默认情况下使用iter.max=10.收敛可能需要十次以上的迭代.
If you are benchmarking K-means, note that R uses iter.max=10 per default. It may take much more than 10 iterations to converge.
这篇关于kmeans:快速转移阶段的步数已超过上限的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！