November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Unsupervised learning
Let's start with an unsupervised machine learning technique, K-means clustering. K-means is a well-known and popular clustering algorithm and works based on the principles of expectation maximization. It belongs to the class of iterative descent clustering methods. Internally, it assumes the variables are of quantitative type and uses Euclidean distance as a similarity measure to arrive at the clusters.
The K is a parameter to the algorithm. K stands for the number of clusters we need. Users need to provide this parameter.
Refer to The Elements of Statistical Learning, Chapter 14 for a more in-depth introduction to the K-means algorithm.
Unsupervised techniques are appealing as they don't require us to build a training ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.