mclustcomp (version 0.3.3)

mclustcomp: Measures for Comparing Clusterings

Description

Given two partitions or clusterings \(C_1\) and \(C_2\), it returns community comparison scores corresponding with a set of designated methods. Note that two label vectors should be of same length having either numeric or factor type. Currently we have 3 classes of methods depending on methodological philosophy behind each. See below for the taxonomy.

Usage

mclustcomp(x, y, types = "all", tversky.param = list())

Arguments

x, y

vectors of clustering labels

types

"all" for returning scores for every available measure. Either a single score name or a vector of score names can be supplied. See the section for the list of the methods for details.

tversky.param

a list of parameters for Tversky index; alpha and beta for weight parameters, and sym, a logical where FALSE stands for original method, TRUE for a revised variant to symmetrize the score. Default (alpha,beta)=(1,1).

Value

a data frame with columns types and corresponding scores.

Category 1. Counting Pairs

TYPE	FULL NAME
'adjrand'	Adjusted Rand index.
'chisq'	Chi-Squared Coefficient.
'fmi'	Fowlkes-Mallows index.
'jaccard'	Jaccard index.
'mirkin'	Mirkin Metric, or Equivalence Mismatch Distance.
'overlap'	Overlap Coefficient, or Szymkiewicz-Simpson coefficient.
'pd'	Partition Difference.
'rand'	Rand Index.
'sdc'	S<U+00F8>rensen<U+2013>Dice Coefficient.
'smc'	Simple Matching Coefficient.
'tanimoto'	Tanimoto index.
'tversky'	Tversky index.
'wallace1'	Wallace Criterion Type 1.

Note that Tanimoto Coefficient and Dice's coefficient are special cases with (alpha,beta) = (1,1) and (0.5,0.5), respectively.

Category 2. Set Overlaps/Matching

TYPE	FULL NAME
'f'	F-Measure.
'mhm'	Meila-Heckerman Measure.
'mmm'	Maximum-Match Measure.

Category 3. Information Theory

TYPE	FULL NAME
'jent'	Joint Entropy
'mi'	Mutual Information.
'nmi1'	Normalized Mutual Information by Strehl and Ghosh.
'nmi2'	Normalized Mutual Information by Fred and Jain.
'nmi3'	Normalized Mutual Information by Danon et al.
'nvi'	Normalized Variation of Information.

References

strehl_cluster_2003mclustcomp

meila_comparing_2007mclustcomp

goos_comparing_2003mclustcomp

wagner_comparing_2007mclustcomp

albatineh_similarity_2006mclustcomp

mirkin_eleven_2001mclustcomp

rand_objective_1971mclustcomp

kuncheva_using_2004mclustcomp

fowlkes_method_1983mclustcomp

dongen_performance_2000mclustcomp

jaccard_distribution_1912mclustcomp

li_combining_2010mclustcomp

larsen_fast_1999mclustcomp

meila_experimental_2001mclustcomp

cover_elements_2006mclustcomp

ana_robust_2003mclustcomp

wallace_comment_1983mclustcomp

simpson_mammals_1943mclustcomp

dice_measures_1945mclustcomp

segaran_programming_2007mclustcomp

tversky_features_1977mclustcomp

danon_comparing_2005mclustcomp

lancichinetti_detecting_2009mclustcomp

Examples

# NOT RUN {
## example 1. compare two identical clusterings
x = sample(1:5,20,replace=TRUE) # label from 1 to 5, 10 elements
y = x                           # set two labels x and y equal
mclustcomp(x,y)                 # show all results

## example 2. selection of a few methods
z = sample(1:4,20,replace=TRUE)           # generate a non-trivial clustering
cmethods = c("jaccard","tanimoto","rand") # select 3 methods
mclustcomp(x,z,types=cmethods)            # test with the selected scores

## example 3. tversky.param
tparam = list()                           # create an empty list
tparam$alpha = 2
tparam$beta  = 3
tparam$sym   = TRUE
mclustcomp(x,z,types="tversky")           # default set as Tanimoto case.
mclustcomp(x,z,types="tversky",tversky.param=tparam)


# }