# Understanding Voting Patterns at AKOS Workshop

Two days ago we held another Introduction to Data Mining workshop at our faculty. This time the target audience was a group of public sector professionals and our challenge was finding the right data set to explain key data mining concepts. Iris is fun, but not everyone is a biologist, right? Fortunately, we found this really nice data set with ballot counts from the Slovenian National Assembly (thanks to Parlameter).

The data contains ballot counts, statistics, and description for 84 members of the parliament (MPs). First, we inspected the data in a Data Table. Each MP is described with 14 meta features and has 18 ballot counts recorded.

We have some numerical features, which means we can also inspect the data in Scatter Plot. We will plot MPs’ attendance vs. the number of their initiatives. Quite interesting! There is a big group of MPs who regularly attend the sessions, but rarely propose changes. Could this be the coalition?

The next question that springs to our mind is – can we discover interesting voting patterns from our data? Let us see. We first explored the data in Hierarchical Clustering. Looks like there are some nice clusters in our data! The blue cluster is the coalition, red the SDS party and green the rest (both from the opposition).

But it is hard to inspect so many data instances in a dendrogram. For example, we have no idea how similar are the voting records of Eva Irgl and Alenka Bratušek. Surely, there must be a better way to explore similarities and perhaps verify that voting patterns exist at even a party-level… Let us try MDS. MDS transforms multidimensional data into a 2D projection so that similar data instances lie close to each other.

Ah, this is nice! We even colored data points by the party. MDS beautifully shows the coalition (blue dots) and the opposition (all other colors). Even parties are clustered together. But there are some outliers. Let us inspect Matej Tonin, who is quite far away from his orange group. Seems like he was missing at the last two sessions and did not vote. Hence his voting is treated differently.

It is always great to inspect discovered groups and outliers. This way an expert can interpret the clusters and also explain, what outliers mean. Sometimes it is simply a matter of data (missing values), but sometimes we could find shifting alliances. Perhaps an outlier could be an MP about to switch to another party.

You can have fun with these data, too. Let us know if you discover something interesting!

# Dimensionality Reduction by Manifold Learning

The new Orange release (v. 3.3.9) welcomed a few wonderful additions to its widget family, including Manifold Learning widget. The widget reduces the dimensionality of the high-dimensional data and is thus wonderful in combination with visualization widgets.

Manifold Learning widget offers five embedding techniques based on scikit-learn library: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding. They each handle the mapping differently and also have a specific set of parameters.

For example, a popular t-SNE requires only a metric (e.g. cosine distance). In the demonstration of this widget, we output 2 components, since they are the easiest to visualize and make sense of.

First, let’s load the data and open it in Scatter Plot. Not a very informative visualization, right? The dots from an unrecognizable square in 2D.

Let’s use embeddings to make things a bit more informative. This is how the data looks like with a t-SNE embedding. The data is starting to have a shape and the data points colored according to regression class reveal a beautiful gradient.

Ok, how about MDS? This is beyond our expectations!

There’s a plethora of options with embeddings. You can play around with ImageNet embeddings and plot them in 2D or use any of your own high-dimensional data and discover interesting visualizations! Although t-SNE is nowadays probably the most popular dimensionality reduction technique used in combination with scatterplot visualization, do not underestimate the value of other manifold learning techniques. For one, we often find that MDS works fine as well.

Go, experiment!

# Ghostbusters

Ok, we’ve just recently stumbled across an interesting article on how to deal with non normal (non-Gaussian distributed) data.

We have an absolutely paranormal data set of 20 persons with weight, height, paleness, vengefulness, habitation and age attributes (.

Let’s check the distribution in Distributions widget.

Our first attribute is “Weight” and we see a little hump on the left. Otherwise the data would be normally distributed. Ok, so perhaps we have a few children in the data set. Let’s check the age distribution.

Whoa, what? Why is the hump now on the right? These distributions look scary. We seem to have a few reaaaaally old people here. What is going on? Perhaps we can figure this out with MDS. This widget projects the data into two dimensions so that the distances between the points correspond to differences between the data instances.

Aha! Now we see that three instances are quite different from all others. Select them and send them to the Data Table for final inspection.

Busted! We have found three ghosts hiding in our data. They are extremely light (the sheet they are wearing must weight around 2kg), quite vengeful and old.

Now, joke aside, what would this mean for a general non-normally distributed data? One thing is your data set might be too small. Here we only have 20 instances, thus 3 outlying ghosts have a great impact on the distribution. It is difficult to hide 3 ghosts among 17 normal persons.

Secondly, why can’t we use Outliers widget to hunt for those ghosts? Again, our data set is too small. With just 20 instances, the estimation variance is so large that it can easily cover a few ghosts under its sheet. We don’t have enough “normal” data to define what is normal and thus detect the paranormal.

Haven’t we just written two exactly opposite things? Perhaps.

Happy Halloween everybody! 🙂