arulesSequences (version 0.2-8)

cspade: 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.

Value

• Returns an object of class sequences.

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.

Class information (on sequences or customers) is taken from component classID, if available.

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.

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, tidLists = TRUE))
summary(s1)
as(s1, "data.frame")

##
summary(tidLists(s1))
transactionInfo(tidLists(s1))

## use timing constraint
s2 <- cspade(zaki, parameter = list(support = 0.4, maxwin = 5))
as(s2, "data.frame")

## use classification
t <- zaki
transactionInfo(t)$classID <-
    transactionInfo(t)$sequenceID %% 2 + 1L
s3 <- cspade(t, parameter = list(support = 0.4, maxwin = 5))
as(s3, "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"))

## 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)