In deduplication, which metric helps detect near-duplicate addresses?

In deduplication, which metric helps detect near-duplicate addresses?

• A. Phone numbers
• B. Geographic proximity
• C. Email domain
• D. IP address

Answer: (B)

Detecting near-duplicate addresses hinges on where a place is located rather than how the address is written. By converting each address into geographic coordinates and measuring how close those points are, you can group records that point to the same location even if the text differs due to abbreviations, typos, or formatting. A distance threshold lets you decide what counts as the same place, capturing near-duplicates effectively. Other signals don’t map reliably to the actual location: phone numbers can move with users, multiple addresses can share a single number; email domains don’t indicate where a place is; IP addresses change with networks and don’t pinpoint a physical address. So geographic proximity directly targets the location, making it the best fit for identifying near-duplicate addresses.

Detecting near-duplicate addresses hinges on where a place is located rather than how the address is written. By converting each address into geographic coordinates and measuring how close those points are, you can group records that point to the same location even if the text differs due to abbreviations, typos, or formatting. A distance threshold lets you decide what counts as the same place, capturing near-duplicates effectively.

Other signals don’t map reliably to the actual location: phone numbers can move with users, multiple addresses can share a single number; email domains don’t indicate where a place is; IP addresses change with networks and don’t pinpoint a physical address. So geographic proximity directly targets the location, making it the best fit for identifying near-duplicate addresses.