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
It’s been a long time coming, but finally we’ve created out our first set of YouTube tutorials. In a series ‘Getting Started with Orange’ we will walk through our software step-by-step. You will learn how to create a workflow, load your data in different formats, visualize and explore the data. These tutorials are meant for complete beginners in both Orange and data mining and come with some handy tricks that will make using Orange very easy. Below are the first three videos from this series, more are coming in the following weeks.
We are also preparing a series called ‘Data Science with Orange’, which will take you on a journey through the world of data mining and machine learning by explaining predictive modeling, classification, regression, model evaluation and much more.
Feel free to let us know what tutorials you’d like to see and we’ll do our best to include it in one of the two series. 🙂