| cspade {arulesSequences} | R Documentation |
Mining frequent sequential patterns with the cSPADE algorithm. This algorithm utilizes temporal joins along with efficient lattice search techniques and provides for timing constraints.
cspade(data, parameter = NULL, control = NULL)
data |
an object of class
transactions with
temporal information. |
parameter |
an object of class SPparameter. |
control |
an object of class SPcontrol. |
Interfaces the command-line tools for preprocessing and mining frequent sequences with the cSPADE algorithm by M. Zaki via a proper chain of system calls.
The temporal information is taken from components sequenceID
(sequence or customer identifier) and eventID (event identifier)
of slot transactionInfo.
The utility function read_baskets provides for reading
of text files with temporal transaction data.
Returns an object of class sequences.
Temporary files may not deleted until the end of the R session if the call is interrupted.
sequenceID and eventID are coerced to factor if necessary.
Christian Buchta, Michael Hahsler
M. J. Zaki. (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning Journal, 42, 31–60.
Class
transactions,
sequences,
SPparameter,
SPcontrol,
method
ruleInduction,
function
read_baskets.
## use example data from paper
data(zaki)
## mine frequent sequences
s1 <- cspade(zaki, parameter = list(support = 0.4),
control = list(verbose = TRUE))
summary(s1)
as(s1, "data.frame")
## use timing constraint
s2 <- cspade(zaki, parameter = list(support = 0.4, maxwin = 5))
as(s2, "data.frame")