From my understanding, clustering algorithms require complete data. Based on this, if there are missing values in my dataset I have two options:
Impute missing information using some sort of imputation method
Get rid of observations that contain missing values
Sometimes I believe neither approach is appropriate, as ignoring observations can remove a lot of important information (sometimes all information, if there is always 1 missing value per obs for example) - and imputation methods are not 100% accurate.
As such, are there any clustering methods that do not require either of these options? In other words, algorithms that use the partial information from observations that don't have complete values, without discarding them? (looking at the information they do have in common)
This would allow me to assign these observations to clusters without applying any imputation methods to the data.