scikit-learn/scikit-learn — pull request #1581 by jaquesgrobler (2013-01-15 16:20:33+00:00)
Title: WIP Example gallery cleanup

This is a bit of a cleanup for the examples gallery (#1284)
There are a few commits that are outstanding for just this moment as I need some feedback on this before I push them - I'll get to that in a moment.

Most of the changes here are small ones like typos/titles too long or missing/whitespace/links related.
Two add-ons for the gallery include:
`animated thumbnails`

![1](https://f.cloud.github.com/assets/1378870/68138/54c7e5c2-5f2a-11e2-8ddb-1fc300179378.jpeg)
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![2](https://f.cloud.github.com/assets/1378870/68131/2f0d6816-5f2a-11e2-9fd4-bfdad1d249fe.jpeg)
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![3](https://f.cloud.github.com/assets/1378870/68133/310c03ac-5f2a-11e2-988a-2eb2bba6b0b5.jpeg)
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and `icons for examples without plots`

![icon](https://f.cloud.github.com/assets/1378870/68147/a598d36c-5f2a-11e2-9854-f8b2177298fe.jpeg)

The pending commits address the following:
1) [Color](http://scikit-learn.org/dev/auto_examples/cluster/plot_color_quantization.html) and [Greyscale](http://scikit-learn.org/dev/auto_examples/cluster/plot_lena_compress.html) Vector Quantization Examples.
I've had a look through both and they are very similiar - I had initially removed the latter and all that links to it - but no I'm not sure if we should. It's quite nice for a beginner to see how it's done on the greyscale 2d example, and then see the colour 3d one. However, if I was to go though with removing the greyscale example - I would then add the histogram to the color example - which, a quick rendering thereof looked like this:
![figure_1](https://f.cloud.github.com/assets/1378870/68157/85d949fc-5f2b-11e2-800a-7c298c190620.png)
**What are your thoughts on this?**

2) The [OLS](http://scikit-learn.org/dev/auto_examples/linear_model/plot_ols.html) and [OLS with SGD](http://scikit-learn.org/dev/auto_examples/linear_model/plot_sgd_ols.html) examples both produce a similiar plot, so I initially just dropped one - but then thought again both are nice for somebody new. So I chose to combine them into one example, making a normal OLS first using the diabetes dataset and then OLS with SGD on the same data. The problem with this is that I had to add about 5000 iterations to SGD with a tiny tolerance for it to get kind of close to the normal OLS plot's result - but still being out-scored..
Thought the plots are fine then, doesn't it look a bit unimpressive in the example gallery?
Let me know what you think here

3) The [Digit Dataset](http://scikit-learn.org/dev/auto_examples/datasets/plot_digits_last_image.html) and [Iris Dataset](http://scikit-learn.org/dev/auto_examples/datasets/plot_iris_dataset.html) examples I removed and just had their references in tutorials point to [plots from other examples](http://scikit-learn.org/dev/auto_examples/plot_digits_classification.html) that display it just as well. But now, the `Datasets` section of the tutorial is rather lonely.. Is that fine or should I leave them?
![foreveralone](https://f.cloud.github.com/assets/1378870/68220/bcaad516-5f2d-11e2-9ab2-b37b64081269.png)

4) There are a few K-means examples, and I'm still torn here.
* [Kmeans clustering](http://scikit-learn.org/dev/auto_examples/cluster/plot_cluster_iris.html)
* [Clustering text documents using k-means](http://scikit-learn.org/dev/auto_examples/document_clustering.html)
* [A demo of K-Means clustering on the handwritten digits data](http://scikit-learn.org/dev/auto_examples/cluster/plot_kmeans_digits.html)
* [A demo of the K Means clustering algorithm](http://scikit-learn.org/dev/auto_examples/cluster/plot_mini_batch_kmeans.html)

The first is the most straight forward. The next too are applications which are useful in my opinion, even though the one is without plot. The last one basically shows a comparison between kmeans and minibatch-k-means.. My fix to this was to remove the first simple Kmeans example, and then use the comparison(last image in list) to serve that purpose too.

I welcome all feedback on this. I've tried to be super-thorough on this, but may have missed something here or there. Cheers