Which type of clustering algorithm to use to identify the "same" item in different data sets?
A: ABC Notebook, Large, Released: 2018-02-20, 150mm x 100mm.
B: Notebook (ABC), L, Date: 18/02/2018, 150mm x 100mm

It doesn't seem like a clustering problem. Rather, it's more like a text mining problem. E.g., you extract &quot;ABC&quot; and &quot;Notebook&quot; from the names of both A and B, and then decide they are the same. Besides that, there are also some format detection needed, like mapping date string to datetime format, L to Large.
After you extracted these features and constructed a feature matrix, you can simply determine if two entries are different items/people by their commonly existing features in the matrix (since there will be NAs in your matrix). You don't really need a machine learning clustering algorithm.

A: Doe, Jane, DOB 1970-06-23, 123 ML Ave, F
B: John Doe, DOB 1971-04-33, 123 ML Avenue, M
C: J. Doe, Born '71

E.g., A and B are not the same since there are mismatches in DOB and sex. B and C can be treated as same, as they both have 2 features - name and DOB - show up in your data set, and both 2 features matched.
You might need a bit more complex algorithm, since there might be a D: John Doe, DOB 1971-03-12, F, with which you should leave B, C, D alone. But that's all it need, I guess.