Orange Blog
By: AJDA, Apr 3, 2017
Image Analytics: Clustering
Data does not always come in a nice tabular form. It can also be a collection of text, audio recordings, video materials or even images. However, computers can only work with numbers, so for any data mining, we need to transform such unstructured data into a vector representation. For retrieving numbers from unstructured data, Orange can use deep network embedders. We have just started to include various embedders in Orange, and for now, they are available for text and images.
By: AJDA, Mar 17, 2017
k-Means and 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.
By: AJDA, Mar 9, 2017
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.
By: AJDA, Mar 8, 2017
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.
By: BLAZ, Mar 6, 2017
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.
By: AJDA, Feb 23, 2017
My First Orange Widget
Recently, I took on a daunting task - programming my first widget. I’m not a programmer or a computer science grad, but I’ve been looking at Orange code for almost two years now and I thought I could handle it. I set to create a simple Concordance widget that displays word contexts in a corpus (the widget will be available in the future release). The widget turned out to be a little more complicated than I originally anticipated, but it was a great exercise in programming.
By: AJDA, Feb 3, 2017
For When You Want to Transpose a Data Table...
Sometimes, you need something more. Something different. Something, that helps you look at the world from a different perspective. Sometimes, you simply need to transpose your data. Since version 3.3.9, Orange has a Transpose widget that flips your data table around. Columns become rows and rows become columns. This is often useful, if you have, say, biological data. Related: Datasets in Orange Bioinformatics Today we will play around with brown-selected.tab, a data set on gene expression levels for 79 experiments.
By: AJDA, Jan 23, 2017
Preparing Scraped Data
One of the key questions of every data analysis is how to get the data and put it in the right form(at). In this post I’ll show you how to easily get the data from the web and transfer it to a file Orange can read. Related: Creating a new data table in Orange through Python First, we’ll have to do some scripting. We’ll use a couple of Python libraries - urllib.
By: AJDA, Jan 13, 2017
Data Preparation for Machine Learning
We’ve said it numerous times and we’re going to say it again. Data preparation is crucial for any data analysis. If your data is messy, there’s no way you can make sense of it, let alone a computer. Computers are great at handling large, even enormous data sets, speedy computing and recognizing patterns. But they fail miserably if you give them the wrong input. Also some classification methods work better with binary values, other with continuous, so it is important to know how to treat your data properly.
By: BLAZ, Dec 22, 2016
The Beauty of Random Forest
It is the time of the year when we adore Christmas trees. But these are not the only trees we, at Orange team, think about. In fact, through almost life-long professional deformation of being a data scientist, when I think about trees I would often think about classification and regression trees. And they can be beautiful as well. Not only for their elegance in explaining the hidden patterns, but aesthetically, when rendered in Orange.