How to deal with with rows with zero in every feature while clustering? I am working on a clustering problem which has 13000 observations and 15 features. Around 3000 observations in the dataset has  zero in every features ( i.e all values zero in 3000 rows). I am trying to do clustering on top of it. What is a better way to deal with it ? I have few things in my mind but would like to get clarity on :


Check for number of rows with all zero and remove them ?
Include the rows with zero value in every feature and let the clustering 
algorithm handle the same?


Also,please suggest if there are any better way to handle the same.

Note : I am using k-means clustering.
It's a matter of data quality so it depends how the dataset was built: 


Either these instances are meaningful, i.e. it makes sense that an observation would have zeros for all the features and that it would happen that often.
Or these are the result of an error, typically the complete absence of measurement for these observations.


Naturally one wants to keep the instances in the data in the former case but (usually) not in the later case, because the values don't represent an actual data point so they would introduce a massive bias for the clustering algorithm.