January 2024
Beginner
672 pages
18h 58m
English
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11 K-means clustering
This chapter covers
- Developing a K-means clustering algorithm
- Computing and visualizing optimal cluster counts
- Understanding standard deviations and computing z-scores
- Creating Cleveland dot plots
Our primary purpose in this chapter is to demonstrate how to develop a K-means clustering algorithm. K-means clustering is a popular unsupervised learning method and multivariate analysis technique that enables purposeful and made-to-order strategies around smart clusters, or groups, cut from the data. Unsupervised learning is a learning method where the goal is to find patterns, structures, or relationships in data using only input variables and therefore no target, or output, data. By contrast, supervised learning methods use ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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.