How does distributed computing improve data mining at scale?

It enables parallel processing of large datasets across multiple machines.

Distributed computing frameworks like Hadoop and Spark divide datasets into partitions and process them in parallel across clusters. This significantly reduces computation time for data mining tasks such as clustering, classification, and association rule mining. Fault tolerance and data locality are key principles ensuring reliability and efficiency.