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For each frequent sequence, the algorithm generates all sequential rules from that frequent sequence.
A frequent sequence is also called a sequential pattern.
The process is repeated until no frequent sequence or no candidate sequence can be found.
It is one of the most efficient methods for frequent sequence mining [10].
For each frequent sequence (f) of size (k), it is possible to generate ((k-1)) rules.
For each frequent sequence (f) of size (k), we can possibly form ((k-1)) rules.
Each node in the prefix-tree stores two pieces of information: label and support, denoted as label: sup, in which label is a frequent sequence and support is the support count of that frequent sequence.
It has to scan the database many times for counting the support of every prefixe of a frequent sequence.
DP-MFSM consists of three phases: pre-processing phase, expected frequent sequence mining (ESM) phase, and candidate extraction and verification (CEV) phase.
So the rules can be generated directly from those frequent sequences without browsing over the whole set of frequent sequence many times.
To better illustrate the problem, consider a frequent sequence of tasks that is stored in the collection of paths: A→B→C→D.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com