Timeline for k-means implementation with custom distance matrix in input
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Dec 14, 2017 at 14:54 | history | edited | kjetil b halvorsen♦ | CC BY-SA 3.0 |
deleted 8 characters in body
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| Nov 22, 2013 at 22:11 | answer | added | Chaitanya Shivade | timeline score: 3 | |
| Apr 26, 2013 at 16:12 | answer | added | szali | timeline score: 3 | |
| Jul 1, 2011 at 8:36 | comment | added | denis | Not Matlab, but the page of python under is-it-possible-to-specify-your-own-distance-function-using-scikits-learn-k-means can use any of the 20-odd metrics in scipy.spatial.distance. | |
| Jun 30, 2011 at 7:20 | answer | added | ttnphns | timeline score: 11 | |
| Jun 30, 2011 at 7:18 | comment | added | steffen | additionally to ttnphns and Not Durrett you might find Is it ok to use Manhattan distance with Ward's inter-cluster linkage in hierarchical clustering? interesting | |
| Jun 30, 2011 at 6:22 | comment | added | ttnphns | You could try to generate raw data corresponding to your matrix of euclidean distances and input those to K-Means. Alternative easy approach could be to use Ward method of hierarchical clustering of the matrix: K-Means and Ward share similar ideology of what a cluster is. | |
| Jun 30, 2011 at 4:50 | answer | added | N F | timeline score: 16 | |
| Jun 30, 2011 at 1:52 | history | asked | Eugenio | CC BY-SA 3.0 |