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!

Orange at Station Houston

With over 262 member companies, Station Houston is the largest hub for tech startups in Houston.

One of its members is also Genialis, a life science data exploration company that emerged from our lab and is now delivering pipelines and user-friendly apps for analytics in systems biology.

Thanks to the invitation by the director of operations Alex de la Fuente, we gave a seminar on Data Science for Everyone. We spoke about how Orange can support anyone to learn about data science and then use machine learning on their own data.

We pushed on this last point: say you walk in downtown Houston, pick first three passersby, take them to the workshop and train them in machine learning. To the point where they could walk out from the training and use some machine learning at home. Say, cluster their family photos, or figure out what Kickstarter project features to optimize to get the funding.

How long would such workshop take? Our informed guess: three hours. And of course, we illustrated this point to seminar attendees by giving a demo of the clustering of images in Orange and showcasing Kickstarter data analysis.

Related: Image Analytics: Clustering

Seminars at Station Houston need to finish with a homework. So we delivered one. Here it is: