Episode mining and sequential pattern mining are both data mining techniques used to uncover patterns in a sequence of events.
The main difference between them is that episode mining seeks to identify patterns that occur in a single sequence, while sequential pattern mining looks for patterns that occur across multiple sequences.
Another difference is that episode mining typically uses a fixed-length window, while sequential pattern mining can use a variable-length window.
Also, episode mining tends to be used for analyzing temporal data, while sequential pattern mining is most often used for analyzing transactional data.
The main difference between them is that episode mining seeks to identify patterns that occur in a single sequence, while sequential pattern mining looks for patterns that occur across multiple sequences.
Another difference is that episode mining typically uses a fixed-length window, while sequential pattern mining can use a variable-length window.
Also, episode mining tends to be used for analyzing temporal data, while sequential pattern mining is most often used for analyzing transactional data.
Statistics: Posted by admin — Tue Jan 31, 2023 1:11 am