Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with k-means
Search options not deleted
user 411113
k-means is a method to partition data into clusters by finding a specified number of means, k, s.t. when data are assigned to clusters w/ the nearest mean, the w/i cluster sum of squares is minimized
1
vote
0
answers
111
views
k-means clustering on a probability distribution instead of a dataset
Normally, clustering algorithms such as $k$-means are defined on a dataset in the following sense: if $D$ is a dataset, find a partition of $D$ into sets $\{S_1, \dots, S_n\}$ that minimises the withi …
- 111