k-Means & Silhouette Score

k-Means is one of the most popular unsupervised learning algorithms for finding interesting groups in our data. It can be useful in customer segmentation, finding gene families, determining document types, improving human resource management and so on.

But… have you ever wondered how k-means works? In the following three videos we explain how to construct a data analysis workflow using k-means, how k-means works, how to find a good k value and how silhouette score can help us find the inliers and the outliers.


#1 Constructing workflow with k-means

#2 How k-means works [interactive visualization]

#3 How silhouette score works and why it is useful

Why Orange?

Why is Orange so great? Because it helps people solve problems quickly and efficiently.

Sašo Jakljevič, a former student of the Faculty of Computer and Information Science at University of Ljubljana, created the following motivational videos for his graduation thesis. He used two belowed datasets, iris and zoo, to showcase how to tackle real-life problems with Orange.

Workshop on InfraOrange

Thanks to the collaboration with synchrotrons Elettra (Trieste) and Soleil (Paris), Orange is getting an add-on InfraOrange, with widgets for analysis of infrared spectra. Its primary users obviously come from these two institutions, hence we organized the first workshop for InfraOrange at one of them.

Some 20 participants spent the first day of the workshop in Trieste learning the basics of Orange and its use for data mining. With Janez at the helm and Marko assisting in the back, we traversed the standard list of visual and statistical techniques and a bit of unsupervised and supervised learning. The workshop was perhaps a bit unusual as most attendees were already quite familiar with these methods, but most haven’t yet used them in such an interactive fashion.

Marko explaining how to analyze spectral data with Orange.


On the second day Marko and Andrej took over and focused on the analysis of spectral data. We demonstrated the use of widgets specifically developed for infrared data and used them with data mining techniques we covered on the first day. After lunch the attendees tried to work on their own data sets, which was a real test for InfraOrange.

Orange for spectral data.


Group photo!


We now have a lot of realistic feedback on what to improve. There is a lot of work to do still, but a week after the workshop the most often occurring bugs have already been fixed.

The future of InfraOrange looks bright and…. khm… well, colorful! 🙂

Orange Workshops: Luxembourg, Pavia, Ljubljana

February was a month of Orange workshops.

Ljubljana: Biologists

We (Tomaž, Martin and I) have started in Ljubljana with a hands-on course for the COST Action FA1405 Systems Biology Training School. This was a four hour workshop with an introduction to classification and clustering, and then with application of machine learning to analysis of gene expression data on a plant called Arabidopsis. The organization of this course has even inspired us for a creation of a new widget GOMapMan Ontology that was added to Bioinformatics add-on. We have also experimented with workflows that combine gene expressions and images of mutant. The idea was to find genes with similar expression profile, and then show images of the plants for which these genes have stood out.

Luxembourg: Statisticians

This workshop took place at STATEC, Luxembourgh’s National Institute of Statistics and Economic Studies. We (Anže and I) got invited by Nico Weydert, STATEC’s deputy director, and gave a two day lecture on machine learning and data mining to a room full of experienced statisticians. While the purpose was to showcase Orange as a tool for machine learning, we have learned a lot from participants of the course: the focus of machine learning is still different from that of classical statistics.

Statisticians at STATEC, like all other statisticians, I guess, value, above all, understanding of the data, where accuracy of the models does not count if it cannot be explained. Machine learning often sacrifices understanding for accuracy. With focus on data and model visualization, Orange positions itself somewhere in between, but after our Luxembourg visit we are already planning on new widgets for explanation of predictions.

Pavia: Engineers

About fifty engineers of all kinds at University of Pavia. Few undergrads, then mostly graduate students, some postdocs and even quite a few of the faculty staff have joined this two day course. It was a bit lighter that the one in Luxembourg, but also covered essentials of machine learning: data management, visualization and classification with quite some emphasis on overfitting on the first day, and then clustering and data projection on the second day. We finished with a showcase on image embedding and analysis. I have in particular enjoyed this last part of the workshop, where attendees were asked to grab a set of images and use Orange to find if they can cluster or classify them correctly. They were all kinds of images that they have gathered, like flowers, racing cars, guitars, photos from nature, you name it, and it was great to find that deep learning networks can be such good embedders, as most students found that machine learning on their image sets works surprisingly well.

We thank Riccardo Bellazzi, an organizer of Pavia course, for inviting us. Oh, yeah, the pizza at Rossopommodoro was great as always, though Michella’s pasta al pesto e piselli back at Riccardo’s home was even better.