From: Recognizing patterns of student’s modeling behaviour patterns via process mining
Algorithm FreSeqPat : Frequent Sequential Pattern Mining |
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Input: A sequence database S, and the minimum support threshold min_sup. |
Output: The complete set of frequent sequential patterns. |
Parameters: α : a sequential pattern; l : the length of α; |
S∥α : the α-projected database, if α≠<>; Otherwise the sequence database S. |
Method: Call FreSeqPat ( <>, 0, S) |
Subroutine: FreSeqPat (α, l, S∥α) |
Scan S∥α; |
If item <b> can be appended to α to form a larger sequential pattern, then |
put item b into set B; |
For each item b in B do |
Append b to α to form a sequential pattern α′; |
Put α′ into set A′; |
For each pattern α′ in A′do |
Construct α-projected database S∥α′; |
Call FreSeqPat (α′, l, S∥α′) |