Timeline for How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?
Current License: CC BY-SA 3.0
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| Aug 31, 2016 at 2:29 | history | edited | Haitao Du | CC BY-SA 3.0 |
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| Aug 31, 2016 at 2:08 | history | edited | Haitao Du | CC BY-SA 3.0 |
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| Aug 31, 2016 at 1:35 | comment | added | Haitao Du | @amoeba $P$ is number of dimensions. I will review the plot and add the code. Thanks. | |
| Aug 30, 2016 at 21:53 | comment | added | ttnphns | I had upvoted because of a demonstration of the euclidean shrinkage phenomenon under high dimensions. But the answer doesn't demonstrate a suffering of k-means clustering from the curse. The suffering would imply that in high dimensions reasonably well separated clusters (and not uniform random data like yours) may fail to be uncovered as succesfully as it is in low dimensions. You didn't touch this topic. | |
| Aug 30, 2016 at 19:05 | comment | added | amoeba | What is $P$?$\,$ | |
| Aug 30, 2016 at 17:36 | history | edited | Haitao Du | CC BY-SA 3.0 |
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| Aug 30, 2016 at 17:23 | history | edited | Haitao Du | CC BY-SA 3.0 |
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| Aug 30, 2016 at 16:55 | history | answered | Haitao Du | CC BY-SA 3.0 |