similarity-methods: Compute Similarities

Description

Provides the generic function similarity and the S4 method to compute similarities among a collection of sequences.

is.subset, is.superset find subsequence or supersequence relationships among a collection of sequences.

Usage

similarity(x, y = NULL, ...)
# S4 method for sequences
similarity(x, y = NULL,
           method = c("jaccard", "dice", "cosine", "subset"),
	   strict = FALSE)
# S4 method for sequences
is.subset(x, y = NULL, proper = FALSE)
# S4 method for sequences
is.superset(x, y = NULL, proper = FALSE)

Arguments

x, y

an object.

…

further (unused) arguments.

method

a string specifying the similarity measure to use (see details).

strict

a logical value specifying if strict itemset matching should be used.

proper

a logical value specifying if only strict relationships (omitting equality) should be indicated.

Value

For similarity, returns an object of class dsCMatrix if the result is symmetric (or method = "subset") and and object of class dgCMatrix otherwise.

For is.subset, is.superset returns an object of class lgCMatrix.

Details

Let the number of common elements of two sequences refer to those that occur in a longest common subsequence. The following similarity measures are implemented:

jaccard:: The number of common elements divided by the total number of elements (the sum of the lengths of the sequences minus the length of the longest common subsequence).
dice:: Uses two times the number of common elements.
cosine:: Uses the square root of the product of the sequence lengths for the denominator.
subset:: Zero if the first sequence is not a subsequence of the second. Otherwise the number of common elements divided by the number of elements in the first sequence.

If strict = TRUE the elements (itemsets) of the sequences must be equal to be matched. Otherwise matches are quantified by the similarity of the itemsets (as specified by method) thresholded at 0.5, and the common sequence by the sum of the similarities.

Examples

Run this code

# NOT RUN {
## use example data
data(zaki)
z <- as(zaki, "timedsequences")
similarity(z)

# require equality
similarity(z, strict = TRUE)

## emphasize common
similarity(z, method = "dice")

## 
is.subset(z)
is.subset(z, proper = TRUE)
# }