Here's a revised version of your Stack Overflow question with some errors corrected and improved clarity:
I have a dataset that represents movements between two locations (origin and destination), and for each location, there are three associated attributes. The dataset structure looks like this:
- movements_count
- o_id
- o_geo_attr1
- o_geo_attr2
- o_geo_attr3
- d_id
- d_geo_attr1
- d_geo_attr2
- d_geo_attr3
- geo_distance
- travel_time
The dataset exhibits non-linearity, and I'd like to compare two datasets with this structure to quantify the distance or similarity between them. I've come across the concept of Hilbert Space, and I'm wondering if this is a suitable method for achieving this purpose. Additionally, are there any simpler or more appropriate methods for this task?