Timeline for Does clustering actually reduce the number of rows in a dataset?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 25 at 4:08 | comment | added | Nick Cox | "Drop" has various meanings in statistical computing, ranging from delete or erase data from the dataset in memory through omit from a model (e.g. exclude a redundant covariate) to ignore for substantive, statistical or scientific purposes. Again, practices vary, but many of us might calculate all possible principal components but then focus reporting on just some of them, all the way down to selecting one as the "best" single summary or plotting two as the "best" reduction to that many dimensions. Here best deserves all the qualifying quotation marks you can give it. | |
| Jun 22 at 11:06 | history | migrated | from math.stackexchange.com (revisions) | ||
| Jun 21 at 11:39 | comment | added | Leox | Yes, after clustering, some actions can be performed to reduce the number of records in the dataset, but this reduction is not the task of the clustering algorithm; it involves other algorithms with their own names. | |
| Jun 21 at 0:53 | history | answered | ConMan | CC BY-SA 4.0 |