Clustering is the process of grouping together data points that are similar to each other based on some predefined metric or similarity measure. It's like trying to organize your GitHub repos by language, except instead of Python and JavaScript, you've got a bunch of multi-dimensional data points that are about as easy to wrangle as a group of interns on their first day.
I tried using clustering to segment our users into cohorts, but it turns out they're all just a bunch of edge cases that don't fit into any neat little boxes, kind of like our codebase.
The data scientist kept going on about how clustering would help us identify customer segments, but I'm pretty sure he just wanted an excuse to play with his shiny new machine learning library he found on GitHub last week.
The 5 Clustering Algorithms Data Scientists Need to Know: This article provides a good overview of the most popular clustering algorithms, including K-means, DBSCAN, and hierarchical clustering. Perfect for when you need to pretend you know what you're talking about in the next team meeting.
Clustering Algorithms: From Start To State Of The Art: This in-depth guide covers the history and evolution of clustering algorithms, from the OG K-means to the latest and greatest deep learning-based approaches. It's like a trip down memory lane, but with more math and less nostalgia.
K-means Clustering: Algorithm, Applications, Evaluation Methods, and Drawbacks: A deep dive into the most popular clustering algorithm, K-means, including its strengths, weaknesses, and how to tune it for optimal performance. Kind of like optimizing your code, but with more trial and error and less Stack Overflow.
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