Timeline for Why is the decision boundary for K-means clustering linear?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Feb 7, 2023 at 23:35 | comment | added | usεr11852 | One could say that the decision boundary is "piecewise linear" if we have multiple classes. | |
| S Nov 17, 2021 at 14:09 | history | suggested | Ahmed Mohamedeen | CC BY-SA 4.0 |
fixed broken link with cached version
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| Nov 17, 2021 at 9:41 | review | Suggested edits | |||
| S Nov 17, 2021 at 14:09 | |||||
| Jan 20, 2018 at 23:20 | comment | added | Bert Kellerman | The boundary is created from multiple hyperplanes, creating convex hulls around the classes, but this doesn't make the boundary itself linear. | |
| Mar 26, 2013 at 13:46 | comment | added | Has QUIT--Anony-Mousse | @juampa technically, it's the squared euclidean distance: it's the sum of squares is minimized by k-means. The square root just happens to be a monotone function, so coincidentially, Euclidean distance is also minimized. | |
| Mar 26, 2013 at 8:55 | comment | added | jpmuc | and you have linear decision boundaries as a consequence of using the Euclidean distance | |
| Mar 26, 2013 at 8:08 | vote | accept | David Faux | ||
| Mar 26, 2013 at 6:34 | history | answered | r_31415 | CC BY-SA 3.0 |