简体繁体 English

首选标签云可视化格式

[英]Preferable Tag Cloud Visualization Formats

原文 2009-08-06 17:18:10 5 4 layout/ user-interface/ tags/ visualization

Out of curiosity, I would love to know what tag clouds formats best serve the purpose of discovery of more and more (relevant)content? 出于好奇，我很想知道哪种标签云格式最能满足发现越来越多（相关）内容的目的？

I am aware of 3 formats, but don't know which one is the best. 我知道3种格式，但是不知道哪种格式最好。

1) delicious one - color shading 1）美味之一 -颜色底纹

2) The standard one with font size variations - 2）具有字体大小变化的标准字体-

3) The one on this site - numbers showing importance/usage. 3）此网站上的一个-显示重要性/用途的数字。

So which ones do you prefer? 那么，您更喜欢哪个呢？ and why? 为什么？

Edit: Thanks to the answers below, I now have much more understanding of tag cloud visualization techniques. 编辑：由于下面的答案，我现在对标签云可视化技术有了更多的了解。

4) Parallel Tag Clouds - a simple use of parallel coordinates technique. 4）平行标签云 -平行坐标技术的简单使用。 I find it more organized and readable. 我发现它更具组织性和可读性。

5) voroni diagram - more useful for identifying tag relationships and making decisions based on them. 5） voroni图 -在识别标签关系并基于它们做出决策时更有用。 Doesn't serves our purpose of discovery of relevant content. 不符合我们发现相关内容的目的。

6) Mind maps - They are good and can be employed to step by step filter content. 6）思维导图-很好，可用于逐步过滤内容。

I found some more interesting techniques here - http://www.cs.toronto.edu/~ccollins/research/index.html 我在这里找到了一些更有趣的技术-http: //www.cs.toronto.edu/~ccollins/research/index.html

4 个解决方案

I really do think that depends on the content of the information and the audience. 我确实认为这取决于信息的内容和受众。 What's relevant to one is not relevant to another. 与一个人相关的与另一个无关。 If an audience is more specialized, then they will be more likely to think along the same lines, but it would still need to be analyzed and catered to by the content provider. 如果受众更加专业，那么他们将更有可能按照相同的思路思考，但是内容提供商仍需要对其进行分析和迎合。

There are also multiple paths that a person can take to "discover more". 一个人可以采取多种途径“发现更多”。 Take the tag "DNS" for example. 以标签“ DNS”为例。 You could drill down to more specific details like "UDP Port 53" and "MX Record", or you could go sideways with terms like "IP address" "Hostname" and "URL". 您可以向下钻取更具体的详细信息，例如“ UDP端口53”和“ MX记录”，也可以使用诸如“ IP地址”，“主机名”和“ URL”之类的术语。 A Voronoi diagram shows clusters, but wouldn't handle the case where general terms could be related to many concepts. Voronoi图显示聚类，但不能处理通用术语可能与许多概念相关的情况。 Hostname mapping to "DNS", "HTTP", "SSH" etc. 主机名映射到“ DNS”，“ HTTP”，“ SSH”等

I've noticed that in certain tag clouds there's usually one or two items that are vastly larger than the others. 我注意到，在某些标签云中，通常有一个或两个项目比其他项目大得多。 Those sorts of things could be served by a mind map, where one central concept has others radiating out from it. 思维导图可以解决这些问题，其中一个核心概念会激发出其他观念。

For the cases of lots of "main topics" where a mind map is inappropriate, there are parallel coordinates but that would be baffling to many net users. 对于思维导图不合适的许多“主要主题”，都有平行坐标，但是这会使许多净用户感到困惑。

I think that if we found an extremely well organized way of sorting clusters of tags while preserving links between generalities and specificities, that would be somewhat helpful to AI research. 我认为，如果我们找到一种对标签簇进行排序的非常有条理的方法，同时又保留了一般性和特殊性之间的联系，那将对AI研究有所帮助。

In terms of which I personally prefer, I think the numeric approach is nice because infrequently referenced tags are still presented at a readable font size. 就我个人而言，我认为数值方法很好，因为不经常引用的标签仍以可读的字体大小显示。 I also think SO does it this way because they have vastly more tags to cover than the average size based cloud a la the standard. 我还认为SO之所以这样做是因为它们比标准的基于平均大小的云覆盖的标签要多得多。

I would go with #2 out of the options you listed above. 在上面列出的选项中，我会选择＃2。

1 - The human eye recognizes and comprehends size differences much more effectively than color, when the color scale is along the same spectrum (ie, various blues as opposed to discrete individual colors). 1-当色标沿着相同的光谱（即，各种蓝色而不是离散的单个颜色）时，人眼比颜色更有效地识别和理解大小差异。
3 - Requires the user to scan the full list and mathematically compare each individual number while scanning. 3-要求用户扫描整个列表，并在扫描时数学比较每个单独的数字。 No real meaningful relationship between tags without a lot of work on the users part. 没有用户方面的大量工作，标签之间就没有真正有意义的关系。

So, going with #2, there are several considerations to take into account: 因此，与＃2一起，需要考虑以下几个方面：

Keep the tags alphabetical. 使标签保持字母顺序。 This affords the user another method of searching and establishes a known relationship between each (assuming they know the alphabet!). 这为用户提供了另一种搜索方法，并在每个方法之间建立了已知的关系（假设他们知道字母！）。 If they're unordered, it's just a crapshoot to find a single one. 如果它们是无序的，那只是找到一个单个的废话。
If size comparison is absolutely critical (this usually isn't the case, as you can scale up each level by a certain percentage or pixel amount), use a monospaced font. 如果大小比较绝对关键（通常不是这种情况，因为您可以按一定百分比或像素数量按比例放大每个级别），请使用等宽字体。 Otherwise, certain letter combinations may end up looking larger than they actually are. 否则，某些字母组合可能最终看起来比实际的更大。
Don't include any commas, pipes, or other dividers. 请勿包含任何逗号，竖线或其他分隔线。 You're already going to have a lot of data in a small area - no need to clutter it up with debris. 您将已经在一个很小的区域中拥有大量数据-无需将碎片弄乱。 Space the tags out with a decent amount of padding, of course. 当然，用适当的填充物隔开标签。 Just don't double the number of visual elements by adding more than just the data. 只是通过添加不仅仅是数据，不要使视觉元素的数量增加一倍。
Set a min/max font size and scale between those. 设置最小/最大字体大小和比例。 There are situations where one tag may be so popular that visually it may appear exponentially larger than the others. 在某些情况下，一个标签可能如此流行，以至于在视觉上它看起来可能比其他标签大得多。 Likewise, you don't want a tag to end up rendering at 1px! 同样，您也不希望标签最终以1px的分辨率渲染！ Set the min/max and adjust between as necessary. 设置最小/最大并根据需要进行调整。

尺寸已调整的voroni图-它显示了哪些标签相互关联

My favorite tag cloud format is the Wordle format. 我最喜欢的标签云格式是Wordle格式。 It looks great and it also does a pretty good job of fitting a lot of tags in a small space. 它看起来很棒，并且在很小的空间内也可以很好地适应很多标签。