Function used to set time constraints and the counting method in methods (
seqe..) for event sequences such as
seqefsub for searching frequent subsequences or
seqeapplysub for checking occurrences of subsequences.
seqeconstraint(maxGap = -1, windowSize = -1, ageMin = -1, ageMax = -1, ageMaxEnd = -1, countMethod = 1)
The maximum time gap between two events
The maximum time span accepted for subsequences
Minimal start time position allowed for subsequences. Ignored when equal to -1 (default).
Maximal start time position allowed for subsequences. Ignored when equal to -1 (default).
Maximal end time position allowed for subsequences. Ignored when equal to -1 (default).
By default, subsequences are counted only
one time by sequence (
ageMaxEnd. If so, two events should not be separated by more
maxGap and the whole subsequence should not exceed a
windowSize time span.
The other parameters specify the start and end age of the subsequence,
it should start between
ageMax and finish
interpreted as the number of positions (time units) from the beginning
of the sequence.
There are 5 options for the
countMethod argument. (1) By default,
the count is the number of sequences that contain the subsequence (
Alternatives are (2)
"CDIST_O" (counts all distinct occurrences in each sequence including possibly overlapping occurrences, i.e., occurrences sharing a same event occurrence), (3)
"CWIN" (number of slidden windows of length
windowSize that contain an occurrence of the subsequence),
"CMINWIN" (number of minimal windows of occurrence) and (5)
"CDIST" (distinct occurrences without event occurrences overlap). See
A constraint object containing one item per constraint type.
Matthias Studer, Nicolas S. M<fc>ller and Reto B<fc>rgin (alternative counting methods) (with Gilbert Ritschard for the help page)
Joshi, Mahesh V., George Karypis, and Vipin Kumar (2001) A Universal Formulation of Sequential Patterns Proceedings of the KDD'2001 Workshop on Temporal Data Mining, San Francisco.
Ritschard, G., A. Gabadinho, N.S. M<fc>ller and M. Studer (2008), Mining event sequences: A social science perspective, International Journal of Data Mining, Modelling and Management, IJDMMM, 1(1), 68-90.