What is the difference between episode mining and sequential pattern mining?
I read some paper that said episode mining is an extension of sequential pattern mining. But after looking at their papers, I am still not sure if they are the same.
I would like to know what is the use of sequential pattern mining? or episode mining?
I have come up with some scenario like this: let's say that a patient visited a doctor more than once within two days, does that need to use episode mining? Or can I achieve this result with sequential pattern mining (or frequent pattern mining)?
Episode and sequential patterns
Re: Episode and sequential patterns
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.