cspade {arulesSequences}R Documentation

Mining Associations with cSPADE

Description

Mining frequent sequential patterns with the cSPADE algorithm. This algorithm utilizes temporal joins along with efficient lattice search techniques and provides for timing constraints.

Usage

cspade(data, parameter = NULL, control = NULL, tmpdir = tempdir())

Arguments

data an object of class transactions with temporal information.
parameter an object of class SPparameter or a named list with corresponding components.
control an object of class SPcontrol or a named list with corresponding components.
tmpdir a non-empty character vector giving the directory name where temporary files are written.

Details

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. Both identifiers must be in (blockwise) ascending order.

The amount of disk space used by temporary files is reported in verbose mode (see class SPcontrol).

The utility function read_baskets provides for reading of text files with temporal transaction data.

Value

Returns an object of class sequences.

Note

Temporary files may not be deleted until the end of the R session if the call is interrupted.

sequenceID and eventID are coerced to factor if necessary.

Author(s)

Christian Buchta, Michael Hahsler

References

M. J. Zaki. (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning Journal, 42, 31–60.

See Also

Class transactions, sequences, SPparameter, SPcontrol, method ruleInduction, function read_baskets.

Examples

## 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")

## replace timestamps
t <- zaki
transactionInfo(t)$eventID <-
    unlist(tapply(seq(t), transactionInfo(t)$sequenceID,
        function(x) x - min(x) + 1), use.names = FALSE)
as(t, "data.frame")
s0 <- cspade(t, parameter = list(support = 0.4))
s0
identical(as(s1, "data.frame"), as(s0, "data.frame"))

## Not run: 
## use generated data
t <- read_baskets(con  = system.file("misc", "test.txt", package =
                                      "arulesSequences"),
                  info = c("sequenceID","eventID","SIZE"))
summary(t)
## use low support
s3 <- cspade(t, parameter = list(support=0.03), 
                control   = list(verbose=TRUE))
summary(s3)
## End(Not run)

[Package arulesSequences version 0.1-4 Index]