How to Abuse p-Values in Correlations

In a parallel universe, not so far from ours, Orange’s Correlation widget looks like this.

Quite similar to ours, except that this one shows p-values instead of correlation coefficients. Which is actually better, isn’t it? I mean, we have all attended Statistics 101, and we know that you can never trust correlation coefficients without looking at p-values to check that these correlations are real, right? So why on Earth doesn’t Orange show them?

First a side note. It was Christmas not long ago. Let’s call a ceasefire on the frequentist vs. Bayesian war. Let us, for Christ’s sake, pretend, pardon, agree that null-hypothesis testing is not wrong per se.

The mantra of null-hypothesis significance testing goes like this:

1. Form hypothesis.
2. Collect data.
3. Test hypothesis.

In contrast, the parallel-universe Correlation widget is (ab)used like this:

1. Collect data.
2. Test all possible hypotheses.
3. Cherry pick those that are confirmed.

This is like the Texas sharpshooter who fires first and then draws targets around the shots. You should never formulate hypothesis based on some data and then use this same data to prove it. Because it usually (surprise!) works.

Illustration by Dirk-Jan Hoek (CC-BY).


Back to the above snapshot. It shows correlations between 100 vegetables based on 100 different measurements (Ca and Mg content, their consumption in Finland, number of mentions in Star Trek DS9 series, likelihood of finding it on the Mars, and so forth). In other words, it’s all made it up. Just a 100×100 matrix of random numbers with column labels from the simple Wikipedia list of vegetables. Yet the similarity between mung bean and sunchokes surely cannot be dismissed (p < 0.001). Those who like bell pepper should try cilantro, too, because it’s basically one and the same thing (p = 0.001). And I honestly can’t tell black bean from wasabi (p = 0.001).

Here are the p-values for the top 100 most correlated pairs.

import numpy as np
import scipy as sp
a = np.random.random((100, 100))
sorted(stats.pearsonr(a[i], a[j])[1] for i in range(100) for j in range(i))[:100]
[0.0002774329730584203, 0.0004158786523819104, 0.0005008536192579852,
0.0007211022164265075, 0.0008268675086438253, 0.0010265740674904762,
(...91 values omitted to reduce the nonsense)
0.01844720610938738, 0.018465602922746942, 0.018662079618069056]

First 100 correlations are highly significant.

To learn a lesson we may have failed to grasp at the NHST 101 class, consider that there are 100 * 99 / 2 pairs. What is the significance of the pair at 5-th percentile?

correlations = sorted(stats.pearsonr(a[i], a[j])[1] for i in range(100) for j in range(i))
npairs = 100 * 99 / 2
print(correlations[int(pairs * 0.05)]

Roughly 0.05. This is exactly what should have happened, because:

correlations[int(npairs * 0.10)]
correlations[int(npairs * 0.15)]
correlations[int(npairs * 0.30)]

This proves only that p-values for the Pearson correlation coefficient are well calibrated (and that Mersenne twister that is used to generate random numbers in numpy works well). In theory, the p-value for a certain statistics (like Pearson’s r) is the probability of getting such or even more extreme value if the null-hypothesis (of no correlation, in our case) is actually true. 5 % of random hypotheses should have a p-value below 0.05, 10 % a value below 10, and 23 % a value below 23.

Imagine what they can do with the Correlations widget in the parallel universe! They compute correlations between all pairs, print out the first 5 % of them and start writing a paper without bothering to look at p-values at all. They know they should be statistically significant even if the data is random.

Which is precisely the reason why our widget must not compute p-values: because people would use it for Texas sharpshooting. P-values make sense only in the context of the proper NHST procedure (still pretending for the sake of Christmas ceasefire). They cannot be computed using the data on which they were found.

If so, why do we have the Correlation widget at all if it’s results are unpublishable? We can use it to find highly correlated pairs in a data sample. But we can’t just attach p-values to them and publish them. By finding these pairs (with assistance of Correlation widget) we just formulate hypotheses. This is only step 1 of the enshrined NHST procedure. We can’t skip the other two: the next step is to collect some new data (existing data won’t do!) and then use it to test the hypotheses (step 3).

Following this procedure doesn’t save us from data dredging. There are still plenty of ways to cheat. It is the most tempting to select the first 100 most correlated pairs (or, actually, any 100 pairs), (re)compute correlations on some new data and publish the top 5 % of these pairs. The official solution for this is a patchwork of various corrections for multiple hypotheses testing, but… Well, they don’t work, but we should say no more here. You know, Christmas ceasefire.

Scatter Plots: the Tour

Scatter plots are surely one of the best loved visualizations in Orange. Very often, when we teach, people go back to scatter plots over and over again to see their data. We took people’s love for scatter plots to the heart and we redesigned them a bit to make them even more friendly.

Our favorite still remains the Informative Projections button. This button helps you find interesting visualizations from all the combinations of your data variables. But what does interesting mean? Well, let us look at an example. Which of the two visualizations tells you more about the data?

We’d say it is the right one. Why? Because now we know that the combination of petal length and petal width nicely separates the classes!

Of course, Informative Projections button will only work when you have set a class (target) variable.

In scatter plot, you can set also the color of the data points (class is selected by default), the size of the points and the shape. This means you can add three new layers of information to your data, but we warn you not to overuse them. This usually looks very incomprehensible, even though it packs a lot of information.

You might notice, that in the current version of Orange, you can no longer select discrete attributes in Scatter Plot. This is entirely intentional. Scatter plots are best at showing the relationship between two numeric variables, such as in the two examples above. Categorical variables are much better represented with Box Plots, histograms (in Distributions) or in Mosaic Display.


Above, we have presented the same information for titanic data set in different visualizations, that are particularly suitable for categorical variables.

Scatter plot also enables so cool tricks. Just like in most visualizations in Orange, I can select a part of the data and observe the subset downstream. Or the other way around. I have a particular subset I wish to observe and I can pass it to Scatter Plot widget, which will highlight selected data instances.

This is also true for all other point-based visualizations in Orange, namely t-SNE, MDS, Radviz, Freeviz, and Linear Projection.

You can see there are many great thing you can do with Scatter Plot. Finally, we have added a nice touch to the visualization.

Yes, setting the size of the attribute is now animated! 🙂

Happy holidays, everyone!

Orange is Getting Smarter

In the past few months, Orange has been getting smarter and sleeker.

Since version 3.15.0, Orange remembers which distinct widgets users like to connect, adjusting the sorting on the widget search menu accordingly. Additionally, there is a new look for the Edit Links window coming soon.

Orange recently implemented a basic form of opt-in usage tracking, specifically targeting how users add widgets to the canvas.

Word cloud of widget popularity in Orange.


The information is collected anonymously for the users that opted-in. We will use this data to improve the widget suggestion system. Furthermore, the data provides us the first insight into how users interact with Orange. Let’s see what we’ve found out from the data recorded in the past few weeks.


There are four different ways of adding a widget to the canvas,

  • clicking it in the sidebar,
  • dragging it from the sidebar,
  • searching for it by right-clicking on canvas,
  • extending the workflow by dragging the communication channel from a widget.


A workflow extend action.


Among Orange users, the most popular way of adding a new widget is by dragging the communication line from the output widget – we think this is the most efficient way of using Orange too. However, the patterns vary among different widgets.

How users add widgets to canvas, from 20,775 add widget events.


Users tend to add root nodes such as File via a click or drag from the sidebar, while adding leaf nodes such as Data Table via extension from another widget.

How users add File to canvas.

How users add Data Table to canvas.


The widget popularity contest goes to: Data Table! Rightfully so, one should always check their data with Data Table.

Widget popularity visualization in Box Plot.


52% of sessions tracked consisted of no widgets being added (the application just being opened and closed). While some people might really like watching the loading screen, most of these are likely due to the fact that usage is not tracked until the user explicitly opts in.


Each bit of collected data comes at a cost to the privacy of the user. Care was put into minimizing the intrusiveness of data collection methods, while maximizing the usefulness of the collected data.

Initially, widget addition events were planned to include a ‘time since application start’ value, in order to be able to plot a user’s actions as a function of time. While this would be cool, it was ultimately decided that its usefulness is outweighed by the privacy cost to users.


For the keen, data is gathered per canvas session, in the following structure:

  • Date
  • Orange version
  • Operating system
  • Widget addition events, each entailing:
    • Widget name
    • Type of addition (Click, Drag, Search or Extend)
    • (Other widget name), if type is Extend
    • (Query), if type is Search or Extend

Data Mining for Anthropologists?

This weekend we were in Lisbon, Portugal, at the Why the World Needs Anthropologists conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry. I was there to hold a workshop on Data Ethnography, an approach that tries to combine methods from data science and anthropology into a fruitful interdisciplinary mix!

Data Ethnography workshop at this year’s Why the World Needs Anthropologists conference.


Data ethnography is a novel methodological approach that tries to view social phenomena from two different points of view – qualitative and quantitative. The quantitative approach is using data mining and machine learning methods on anthropological data (say from sensors, wearables, social media, online fora, field notes and so on) trying to find interesting patterns and novel information. The qualitative approach uses ethnography to substantiate the analytical findings with context, motivations, values, and other external data to provide a complete account of the studied phenomenon.

At the workshop, I presented a couple of approaches I use in my own research, namely text mining, clustering, visualization of patterns, image analytics, and predictive modeling. Data ethnography can be used, not only in its native field of computational anthropology, but also in museology, digital anthropology, medical anthropology, and folkloristics (the list is probably not exhaustive). There are so many options just waiting for the researchers to dig in!

Related: Text Analysis Workshop at Digital Humanities 2017

However, having data- and tech-savvy anthropologists does not only benefit the research, but opens a platform for discussing the ethics of data science, human relationships with technology, and overcoming model bias. Hopefully, the workshop inspired some of the participants to join me on a journey through the amazing expanses of data science.

To get you inspired, here are two contributions that present some option for computational anthropological research: Data Mining Workspace Sensors: A New Approach to Anthropology and Power of Algorithms for Cultural Heritage Classification: The Case of Slovenian Hayracks.


Orange Now Speaks 50 Languages

In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian… Language support was most evident in the list of stopwords, normalization and POS tagging.

Related: Text Workshops in Ljubljana

Stopwords come from NLTK library, so we can only offer whatever is available there. However, TF-IDF already implicitly considers stopwords, so the functionality is already implemented. For POS tagging, we would rely on Stanford POS tagger, that already has pre-trained models available.

The main issue was with normalization. While English can do without lemmatization and stemming for simple tasks, morphologically rich languages, such as Slovenian, perform poorly on un-normalized tokens. Cases and declensions present a problem for natural language processing, so we wanted to provide a tool for normalization in many different languages. Luckily, we found UDPipe, a Czech initiative that offers trained lemmatization models for 50 languages. UDPipe is actually a preprocessing pipeline and we are already thinking about how to bring all of its functionality to Orange, but let us talk a bit about the recent improvements for normalization.

Let us load a simple corpus from Corpus widget, say that contain 44 tales from the Grimm Brothers. Now, pass them through Preprocess Text and keep just the defaults, namely lowercase transformation, tokenization by words, and removal of stopwords. Here we see that we have came as quite a frequent word and come as a bit less frequent. But semantically, they are the same word from the verb to come. Shouldn’t we consider them as one word?

Results without applying normalization.


We can. This is what normalization does – it transforms all words into their lemmas or basic grammatical form. Came and come will become come, sons and son will become son, pretty and prettier will become pretty. This will result in less tokens that capture the text better, semantically speaking.

Results of UDPipe normalization.


We can see that came became come with 435 counts. Went became go. Said became say. And so on. As we said, this doesn’t work only on verbs, but on all word forms.

One thing to note here. UDPipe has an internal tokenizer, that works with sentences instead of tokens. You can enable it by selecting UDPipe tokenizer option. What is the difference? A quicker version would be to tokenize all the words and just look up their lemma. But sometimes this can be wrong. Consider the sentence:

I am wearing a tie to work.

Now the word tie is obviously a piece of clothing, which is indicated by the word wearing before it. But tie alone can also be the verb to tie. So the UDPipe tokenizer will consider the entire sentence and correctly lemmatize this word, while lemmatization on regular tokens might not. While UDPipe works better, it is also slower, so you might want to work with regular tokenization to speed up the analysis.

In Preprocess Text, you turn on the Normalization button on the right, then select UDPipe Lemmatizer and select the language you wish to use. Finally, if you wish to go with the better albeit slower UDPipe tokenizer, tick the UDPipe tokenizer box.


Finally, UDPipe does not remove punctuation, so you might end up with words like rose. and away., with the full stop at the end. This you can fix with using regular tokenization and also by select the Regex option in Filtering, which will remove pure punctuation.

Final workflow, where we compared the results of no normalization and UDPipe normalization in a word cloud.


This is it. UDPipe contains lemmatization models for 50 languages and only when you click on a particular language in the Language option, will the resource be loaded, so your computer won’t be flooded with models for languages you won’t ever use. The installation of UDPipe could also be a little tricky, but after some initial obstacles, we have managed to prepare packages for both pip (OSX and Linux) and conda (Windows).

We hope you enjoy the new possibilities of a freshly multilingual Orange!

Orange in Space

Did you know that Orange has already been to space? Rosario Brunetto (IAS-Orsay, France) has been working on the analysis of infrared images of asteroid Ryugu as a member of the JAXA Hayabusa2 team. The Hayabusa2 asteroid sample-return mission aims to retrieve data and samples from the near-Earth Ryugu asteroid and analyze its composition. Hayabusa2 arrived at Ryugu on June 27 and while the spacecraft will return to Earth with a sample only in late 2020, the mission already started collecting and sending back the data. And of course, a part of the analysis of Hayabusa’s space data has been done in Orange!

An image of the asteroid Ryugu acquired by the Hayabusa2 (©JAXA).


Within the Hayabusa2 project, near-infrared spectral data will be collected in three series: the first part is the macro data from remote sensing measurements that are being collected at different altitudes from the asteroid by the Japanese spectrometer NIRS3 (©JAXA). The second part is surface infrared imaging at the micron scale that will soon be performed (October 2018) by the French MicrOmega instrument on the lander MASCOT (DLR-CNES). The third part are the samples that will be analyzed upon return. Among the techniques that will be used in different laboratories around the world in 2021 to analyze the returned samples are the hyperspectral imaging and micro-tomography with an infrared imaging FPA microscope, that will be performed by the IAS team at SMIS-SOLEIL. This means the data will contain satellite spectral images as well as microscope measurements.

Dr. Brunetto is currently working with the first part of the data, namely the macro hyperspectral images of the asteroid. Several tens of thousands of spectra over 70 spectral channels have already been acquired. The main goal of this initial exploration was to constrain the surface composition.

Once the data was preprocessed and cleaned in Python, separate surface regions were extracted in Orange with k-Means and PCA and plotted with the HyperSpectra widget, which comes as a part of the Spectroscopy package. So why was Orange chosen over other tools? Dr. Brunetto says Orange is an easy and friendly tool for complicated things, such as exploring the compositional diversity of the asteroid at the different scales. There are many clustering techniques he can use in Orange and he likes how he can interactively change the number of clusters and the changes immediately show in the plot. This enables the researchers to determine the level of granularity of the analysis, while they can also immediately inspect how each cluster looks like in a hyperspectra plot.

Moreover, one can quickly test methods and visualize the effects and at the same time have a good overview of the workflow. Workflows can also be reused once the new data comes in or, if the pipeline is standard, used on a completely different data set!

A simple workflow for the analysis of spectral data. 😁 A great thing about Orange is that you can label parts of the workflow and explore a different aspect of the data in each branch!


We would of course love to show you the results of the asteroid analysis, but as the project is still ongoing, the data is not yet available to the public. Instead, we asked Zélia Dionnet, dr. Brunetto’s PhD student, to share the results of her work on the organic and mineralogic heterogeneity of the Paris meteorite, which were already published.

She analyzed the composition of the Paris meteorite, which was discovered in 2008 in a statue. The story of how the meteorite was found is quite interesting in itself, but we wanted to know more on how the sample was analyzed in Orange. Dionnet had a slightly larger data set, with 16,000 spectra and 1600 wavenumbers. Just like dr. Brunetto, she used k-Means to discover interesting regions in the sample and Hyperspectra widget to plot the results.

k-Means clusters plotted in the HyperSpectra widget.


At the top, you can see a 2D map of the meteorite sample showing the distribution of the clusters that were identified with k-Means. At the bottom, you see cluster averages for the spectra. The green region is the most interesting one and it shows crystalline minerals, which formed billions of years ago as the hydrothermal processes in the asteroid parent body of the meteorite turned amorphous silicates into phyllosilicates. The purple, on the contrary, shows different micro-sized minerals.

This is how to easily identify the compositional structure of samples with just a couple of widgets. Orange seems to love going to space and can’t wait to get its hands dirty with more astro-data!


Text Workshops in Ljubljana

In the past month, we had two workshops that focused on text mining. The first one, Faksi v praksi, was organized by the University of Ljubljana Career Centers, where high school students learned about what we do at the Faculty of Computer and Information Science. We taught them what text mining is and how to group a collection of documents in Orange. The second one took on a more serious note, as the public sector employees joined us for the third set of workshops from the Ministry of Public Affairs. This time, we did not only cluster documents, but also built predictive models, explored predictions in nomogram, plotted documents on a map and discovered how to find the emotion in a tweet.

These workshops gave us a lot of incentive to improve the Text add-on. We really wanted to support more languages and add extra functionalities to widgets. In the upcoming week, we will release the 0.5.0 version, which introduces support for Slovenian in Sentiment Analysis widget, adds concordance output option to Concordances and, most importantly, implements UDPipe lemmatization, which means Orange will now support about 50 languages! Well, at least for normalization. 😇

Today, we will briefly introduce sentiment analysis for Slovenian. We have added the KKS 1.001 opinion corpus of Slovene web commentaries, which is a part of the CLARIN infrastructure. You can access it in the Corpus widget. Go to Browse documentation corpora and look for Let’s have a quick view in a Corpus Viewer.

The data comes from comment sections of Slovenian online media and contains a fairly expressive language. Let us observe, whether a post is negative or positive. We will use Sentiment Analysis widget and select the Liu Hu method for Slovenian. This is a dictionary based method, where the algorithm sums the positive words and subtracts the sum of negative words. This gives a final score of the post.

We will have to adjust the attributes for a nicer view in a Select Columns widget. Remove all attributes other than sentiment.

Finally, we can observe the results in a Heat Map. The blue lines are the negative posts, while the yellow ones are positive. Let us select the most positive tweets and see, what they are about.

Looks like Slovenians are happy, when petrol gets cheaper and sports(wo)men are winning. We can relate.

Of course, there are some drawbacks of lexicon-based methods. Namely, they don’t work well with phrases, they often don’t consider modern language (see ‘Jupiiiiiii’ or ‘Hooooooraaaaay!’, where the more the letters, the more expressive the word is) and they fail with sarcasm. Nevertheless, even such crude methods give us a nice glimpse into the corpus and enable us to extract interesting documents.

Stay tuned for the information on the release date and the upcoming post on UDPipe infrastructure!

Explaining Kickstarter Success

On Kickstarter most app ideas don’t get funded. But why is that? When we are looking for possible explanations, it is easy to ascribe the failure to the type of the idea.

But what about those rare cases, where an app idea gets funded? Can we figure out why a particular idea succeeded? Our new widget Explain Predictions can do just that – explain why they will succeed. Or at least, explain why the classifier thinks they will.

First, let us load the Kickstarter data from the Datasets widget and inspect it in a Data Table.

Select the data instance you wish to explore in a Data Table.

Now, let’s see why the app Create Games & Apps Without Any Coding got funded.

Explain Predictions needs 3 inputs. Our data set, a classifier and a data sample that we wish to inspect. Connect File widget with Explain Predictions. Then add the classifier, say, Logistic Regression. Finally, select Create Games & Apps Without Any Coding in the Data Table and connect it to the widget.

Explain Predictions needs three inputs.

The highest ranking attributes are those that contributed the most (high Score value). The fact that there were 11 pledge levels, 13 images, many connections to other projects and the length of the project description – all of these attributes add something positive to the funding. On the other side, we see how the duration of the project, description length, maximal pledge tiers and the type of the idea work against the decision to fund the project. Lastly, not having a Facebook page or a video amounts to almost nothing in the making of the final prediction.

High score means the attribute contributed positively to the the final decision (Funded: yes), while low scores contributed negatively.

When explaining the decision of the classifier, we look at the values of the attributes for our sample and how they interact. We do that by approximating Shapely value, since calculating it exactly would sometimes take more then a lifetime. That means customized explanations for every individual case, while treating classifier like a black box. You could do the same for any model the Orange offers, including Neural Networks!

And there you have it, an easy way to know what makes your Kickstarter campaign succeed, cell be classified as healthy, or a bank loan approved.

Data Mining and Machine Learning for Economists

Last week Blaž, Marko and I held a week long introductory Data Mining and Machine Learning course at the Ljubljana Doctoral Summer School 2018. We got a room full of dedicated students and we embarked on a journey through standard and advanced machine learning techniques, all presented of course in Orange. We have covered a wide array of topics, from different clustering techniques (hierarchical clustering, k-means) to predictive models (logistic regression, naive Bayes, decision trees, random forests), regression and regularization, projections, text mining and image analytics.

Related: Data Mining for Business and Public Administration

Definitely the biggest crowd-pleaser was the Geo add-on in combination with the HDI data set. First, we got the HDI data from Datasets. A quick glimpse into a data table to check the output. We have information on some key performance indicators gathered by the United Nations for 188 countries. Now we would like to know which countries are similar based on the reported indicators. We will use Distances with Euclidean distance and use Ward linkage in Hierarchical Clustering.


In Datasets widget we have selected the HDI data set.


The HDI data set contains information on 188 countries, which are described with 66 features. The data set can be used for regression, but we will perform clustering to discover countries, similar by the proposed parameters.


We got our results in a dendrogram. Interestingly, the United States seems similar to Cuba. Let us select this cluster and inspect what the most significant feature for this cluster. We will use the Data output of Hierarchical Clustering which append a column indicating whether the data instances was selected or not. Then we will use Box Plot, group by Selected and check Order by relevance. It seems like these countries have the longest life expectancy at age 59. Go ahead and inspect other clusters by yourself!

Select an interesting cluster in Hierarchical Clustering.


And inspect the results in a box plot. Seems like the selected cluster stands out from the other countries by high life expectancy.


Of course, when we are talking about countries one naturally wants to see them on a map! That is easy. We will use the Geo add-on. First, we need to convert all the country names to geographical coordinates. We will do this with Geocoding, where we will encode column Country to latitude and longitude. Remember to use the same output as before, that is Data to Data.

Use Encode to convert a column with region identifiers (in our case Country) to latitude/longitude pairs.


Now, let us display these countries on a map with Choropleth widget. Beautiful. It is so easy to explore country data, when you see it on a map. You can try coloring also by HDI or any other feature.

Choropleth shows us which countries were in the selected cluster (red). We used Selected as attribute and colored by Mode.


The final workflow:

We always try to keep our workshops fresh and interesting and visualizations are the best way to achieve this. Till the next workshop!







Girls Go Data Mining

This week we held our first Girls Go Data Mining workshop. The workshop brought together curious women and intuitively introduced them to essential data mining and machine learning concepts. Of course, we used Orange to explore visualizations, build predictive models, perform clustering and dive into text analysis. The workshop was supported by NumFocus through their small development grant initiative and we hope to repeat it next year with even more ladies attending!

Related: Text Analysis for Social Scientists

In two days, we covered many topics. On day one, we got to know Orange and the concept of visual programming, where the user construct analytical workflow by stacking visual components. Then we got to know several useful visualizations, such as box plot, scatter plot, distributions, and mosaic display, which give us an initial overview of the data and the potentially interesting patterns. Finally, we got our hands dirty with predictive modeling. We learnt about decision trees, logistic regression, and naive Bayes classifiers, and observed the models in tree viewer and nomogram. It is great having interpretable models and we had great fun exploring what is in the model!

On the second day, we tried to uncover groups in our data with clustering. First, we tried hierarchical clustering and explored the discovered clusters with box plot. Then we also tried k-means and learnt, why this method is better than hierarchical clustering. In the final part, we talked about the methods for text mining, how to do preprocessing, construct a bag of words and perform the machine learning on corpora. We used both clustering and classification and tried to find interesting information about Grimm tales.

One of our workflows, where we explored the data in many different ways, including inspecting misclassifications in a scatter plot!


One thing that always comes up as really useful in our workshops is Orange’s ability to output different types of data. For example, in Hierarchical Clustering, we can select the similarity cutoff at the top and output clusters. Our data table will have an additional column Cluster, with cluster labels for each data instance.


Hierarchial Clustering outputs data with an additional Cluster column.


We can explore clusters by connecting a Box Plot to Hierarchical Clustering, selecting Cluster in Subgroups and using Order by relevance option. This sorts the variables in Box Plot by how well they separate between clusters or, in other words, what is typical of each cluster.

We have selected Cluster in Subgroups section and ticked ‘Order by relevance’ to sort the variables. Variables at the top are the most interesting ones. Looks like giving milk is an exclusive property of cluster C1.


We used and made the cutoff at three clusters. It looks like the first cluster gives milk. Could these be a cluster of mammals?

We said giving milk is a property of cluster C1. By selecting type as our variable, we can see that C1 is a cluster of mammals.


Indeed it is!

Another option is to select a specific cluster in the dendrogram. Then, we have to rewire the connection between Hierarchical Clustering and Box Plot by setting it to Data. Data option will output the entire data set, with an extra column showing whether the data instance was selected or not. In our case, there would be a Yes if the instance is in the selected cluster and No if it is not.

To rewire the connection, double-click on it and drag a line from Data to Data.


We have selected one cluster in the dendrogram, rewired the connection to transmit Data (instead of Selected Data) and observed the results in a Data Table. We see an additional Selected column, which shows whether a data instance was selected in the visualization or not.


Then we can use Box Plot to observe what is particular for our selected cluster.

In this Box Plot we have used Selected in the Subgroups section and kept ‘Order by relevance’ on. The suggested distinctive feature of our selected cluster is having feathers.


It looks like animals from our selected cluster have feathers. Probably, this is a cluster of birds. We can check this with the same procedure as above.

In summary, most Orange visualizations have two outputs – Selected Data and Data. Selected Data will output a subset of data instances selected in the visualization (or selected clusters in the case of hierarchical clustering), while Data will output the entire data table with a column defining whether a data instance was selected or not. This is very useful if we want to inspect what is typical of an interesting group in our data, inspect clusters or even manually define groups.

Overall, this was another interesting workshop and we hope to continue our fruitful partnership with NumFocus and keep offering free educational events for beginners and experts alike!