关于标签云中标签的缩放
我正在移动设备上实现标签云。数据模型等细节在这里并不是特别重要。我的问题是关于标签的缩放：
将标签频率映射到字体大小的“最佳”表达式是什么？
我已经查看了这篇文章讨论线性和对数缩放和这个答案来自 Adrian Kuhn 的多项式方法灵感草图。然而，我似乎记得互联网上某个地方的一篇文章，对这个问题进行了更多的探索。
我还发现了一些“最佳实践”在博客上，尽管我不确定最佳实践的规定。这些对频率缩放没有评论。
对于标签缩放，我有哪些替代方法？哪种是首选/标准方法？我还在考虑最小字体大小、最大标签数量、颜色等。
编辑：根据 这个问题，我对带有字体大小变化的“标准”标签云感兴趣。
I am implementing a tag cloud on a mobile device. The details of data-model etc, are not particularly important here. My question is about the scaling of tags:
What is the 'best' expression to map tag frequency to font size?
I have looked at this post discussing linear and logarithmic scaling and this answer from Adrian Kuhn sketch of a polynomial approach for inspiration. However, I seem to remember a post some place on the interwebs with a lot more exploration on this issue.
I have also found some "best practices" on a blog, though am unsure of the providence of the best practices. These make no comment on frequency scaling.
What alternatives do I have for tag scaling, and which is the preferred/standard method? I am also considering minimum fontsizes, maximum number of tags, colors, etc.
Edit: As per the discussion in this question, I am interested in the "standard" tagcloud, with font size variations.
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去年我参与了一个小型标签云项目，其中我使用了类似的东西
where ω is a weighting previously calculated according to some metric (in your case font frequency), minθ and maxθ are lower and upper bounds, and β is the final value. This can be applied to any visual characteristic (font size, colour, weight if supported, etc.).
我发现线性和对数缩放往往依赖于数据集分布。在具有突出异常值的数据集中，我发现 tanh 对于“平滑”结果值很有用。
I worked on a small tag cloud project last year, in which I used something along the lines of
where ω is a weighting previously calculated according to some metric (in your case font frequency), minθ and maxθ are lower and upper bounds, and β is the final value. This can be applied to any visual characteristic (font size, colour, weight if supported, etc.).
I found that linear and logarithmic scaling tended to dependant on data set distribution. In data sets with prominent outliers I found tanh was useful for 'smoothing' the resulting values.
此 pdf 中有一个精彩的讨论，其中讨论了要显示的标签的缩放、聚类和截断。
There is an excellent discussion in this pdf, which discusses scaling, clustering, and truncating on the tags to display.
我发现效果很好的解决方案如下
font_size = (max_font_size - min_font_size) * (Math.sin(1.5*(X)) + minsize其中 X 是您希望映射到字体大小的标准化值
X = (this_value-min_value)/(max_value-min_value)这会增加范围内较低 3 个四分位数的大小差异，以便最大限度地减少高异常值的影响
A solution I found works nicely is as follows
font_size = (max_font_size - min_font_size) * (Math.sin(1.5*(X)) + minsizewhere X is the normalized value you wish to map onto font size
X = (this_value-min_value)/(max_value-min_value)this increases size differential for the lower 3 quartiles of the range, such as to minimize the effect of high outliers