Answer: (ii) asymmetric binary attribute

The Jaccard coefficient is defined as:
$$J(x, y) = \frac{\text{number of 1-1 matches}}{\text{number of attributes that are 1 in at least one object}} = \frac{f_{11}}{f_{01} + f_{10} + f_{11}}$$

It deliberately ignores 0-0 matches (f_00). This is the right choice when one value (say, 1 = "present") is much rarer and more meaningful than the other (0 = "absent") — i.e., an asymmetric binary attribute, like "bought item" (1) vs "didn't buy" (0) in market-basket data. Counting the huge number of 0-0 matches (both didn't buy) would dilute similarity meaninglessly.

For symmetric binary attributes (both outcomes equally important, e.g., gender M/F), the Simple Matching Coefficient (SMC) is used instead, since it includes 0-0 matches.