Watchhttps://www.youtube.com/watch?v=JnnaDNNb380Links to an external site.for a very basic overview of supervised vs. unsupervised learning as well as an intro to k-means clustering. Disregard the last part about autoencoders–that’s beyond the scope of this course.
Open Orange with a blank canvas/project.
Drop a ‘datasets’ widget onto the canvas.
Configure it to pull the Iris dataset. (find it, then double-click on it)
Connect a data table widget to ensure that you’re getting the correct data.
From the data table widget, connect a k-means widget (blue, unsupervised learning).
Configure it for three (3) fixed clusters.
Connect a scatter plot widget to visualize the output, selecting the “Color” option to use the Cluster.
Leaving that window open, go back to the k-means widget config and change the number of clusters–2, 4, etc. to see what happens.
Your submission will be:

a minimum of 100 words explaining the basic functionality of k-means, including your observations from changing the ‘k’ parameter (the number of target clusters)

For a good (and corny but entertaining) explanation of k-means, watchhttps://youtu.be/4b5d3muPQmALinks to an external site.Minimize Video 

a screenshot of your scatter plot with 3 clusters (k=3)
a single Word document with your written explanation and screenshot