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Hi all,
I have bidding data and I like to find if there are bidders with similar behavior.
More specifically, let say we have the following sample data
Date | Time | bidder | bid | |||
1/1/2021 | 08:15:00 | user1 | 23 | |||
1/1/2021 | 09:30:13 | user1 | 25 | |||
1/1/2021 | 11:22:45 | user1 | 24 | |||
1/1/2021 | 08:02:55 | user2 | 23 | |||
1/1/2021 | 10:13:49 | user2 | 24 | |||
1/1/2021 | 11:45:06 | user2 | 24 | |||
1/1/2021 | 13:15:00 | user3 | 28 | |||
1/1/2021 | 13:18:22 | user3 | 27 | |||
1/1/2021 | 14:55:47 | user3 | 27 | |||
1/1/2021 | 14:59:32 | user3 | 28 | |||
1/1/2021 | 16:28:45 | user3 | 29 |
The desired result is a metric evaluating the similarity of the bidders. As we can observe user1 and user2 have similar bidding pattern (with time shift), while user3 is unrelated. I started thinking about date time warping algorithms or correlation matrix but I don't know if there is a better approch.
I would greatly appreciate any suggestion. Thank you in advance!
Solved! Go to Solution.
Hi @Anonymous thanks for your response.
I'm already examining some options to incorporate python and ML to my visualizations.
Hi @Anonymous
If you want similarity patterns, then you must employ Machine Learning algorithms. DAX will not give you that. It must come from outside when you process your data or... you have to somehow use Python/M in your visuals. Unless, of course, you can define clear programming rules for DAX. But if your patterns are complex and call for ML algorithms, well, then DAX is not the tool.
Hi @Anonymous thanks for your response.
I'm already examining some options to incorporate python and ML to my visualizations.
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