#93: K-means Clustering: Machine Learning Algorithm To Easily Split Observations Into Multiple Buckets Around IT In 256 Seconds podcast

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#93: K-means clustering: machine learning algorithm to easily split observations into multiple buckets

1+ y ago 4:16

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K-means clustering is an algorithm for partitioning data into multiple, non-overlapping buckets. For example, if you have a bunch of points in two-dimensional space, this algorithm can easily find concentrated clusters of points. To be honest, that’s quite a simple task for humans. Just plot all the points on a piece of paper and find areas with higher density. For example, most of the points are located on the top-left of the plane, some at the bottom and a few at the centre-right. However, this is not that straightforward once you can no longer rely on graphical representation. For instance, when your data points live 3-, 4- or 100-dimensional space. Turns out, this is not that uncommon. Let me clarify.

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#93: K-means clustering: machine learning algorithm to easily split observations into multiple buckets

Around IT in 256 seconds

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published 1+ y ago

Bagikan

M4A•Beranda episode

Konten disediakan oleh Tomasz Nurkiewicz. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Tomasz Nurkiewicz atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

K-means clustering is an algorithm for partitioning data into multiple, non-overlapping buckets. For example, if you have a bunch of points in two-dimensional space, this algorithm can easily find concentrated clusters of points. To be honest, that’s quite a simple task for humans. Just plot all the points on a piece of paper and find areas with higher density. For example, most of the points are located on the top-left of the plane, some at the bottom and a few at the centre-right. However, this is not that straightforward once you can no longer rely on graphical representation. For instance, when your data points live 3-, 4- or 100-dimensional space. Turns out, this is not that uncommon. Let me clarify.

Read more: https://nurkiewicz.com/93

Get the new episode straight to your mailbox: https://nurkiewicz.com/newsletter

… continue reading

98 episode

#Tech #Tomasz Nurkiewicz

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