Is my data appropriate for Hierarchical Clustering?

I am a newbie in clustering and trying to check whether there are differences in Symptoms (example: cough, sneezing, shortness of breath, etc) reported across different comorbidity groups ( obesity, asthma, etc).

My data is in two formats:

• original one – with 53.000 rows
• and aggregate one obtained from the 53.000 k rows data – frequency and percentages of symptoms across comorbidities – which leads to an aggregate data of 9 rows of comorbidities, and 17 rows of symptoms (I have chosen the percentage instead of counts).

I have already done some hierarchical clustering on the aggregate level – yet I am wondering whether this is okay? I have chosen to do hierarchical clustering on 5000 rows only, but very computational expensive.

Thus in summary my questions are:

1. is aggregate level data (2nd type of data outlined above) okay for clustering? I have actually 4 clusters of comorbidities given by aglomerative one. And hope the choice here is good one.

2. as I have done some clustering on original data, not sure why this is so slow when run on 10.000 let alone 53.000. I have therefore decreased it to 5,000 as this was the best choice for me. is this behaviour on 53.000 rows okay?

Thank you in advance for your help.