mclustcomp | R Documentation |
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.
mclustcomp(x, y, types = "all", tversky.param = list())
x, y |
vectors of clustering labels |
types |
|
tversky.param |
a list of parameters for Tversky index; |
a data frame with columns types
and corresponding scores
.
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' | Sørensen–Dice Coefficient. |
'smc' | Simple Matching Coefficient. |
'tanimoto' | Tanimoto index. |
'tversky' | Tversky index. |
'wallace1' | Wallace Criterion Type 1. |
'wallace2' | Wallace Criterion Type 2. |
Note that Tanimoto Coefficient and Dice's coefficient are special cases with (alpha,beta) = (1,1) and (0.5,0.5), respectively.
TYPE | FULL NAME |
'f' | F-Measure. |
'mhm' | Meila-Heckerman Measure. |
'mmm' | Maximum-Match Measure. |
'vdm' | Van Dongen Measure. |
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. |
'vi' | Variation of Information. |
strehl_cluster_2003mclustcomp
\insertRefmeila_comparing_2007mclustcomp
\insertRefgoos_comparing_2003mclustcomp
\insertRefwagner_comparing_2007mclustcomp
\insertRefalbatineh_similarity_2006mclustcomp
\insertRefmirkin_eleven_2001mclustcomp
\insertRefrand_objective_1971mclustcomp
\insertRefkuncheva_using_2004mclustcomp
\insertReffowlkes_method_1983mclustcomp
\insertRefdongen_performance_2000mclustcomp
\insertRefjaccard_distribution_1912mclustcomp
\insertRefli_combining_2010mclustcomp
\insertReflarsen_fast_1999mclustcomp
\insertRefmeila_experimental_2001mclustcomp
\insertRefcover_elements_2006mclustcomp
\insertRefana_robust_2003mclustcomp
\insertRefwallace_comment_1983mclustcomp
\insertRefsimpson_mammals_1943mclustcomp
\insertRefdice_measures_1945mclustcomp
\insertRefsegaran_programming_2007mclustcomp
\insertReftversky_features_1977mclustcomp
\insertRefdanon_comparing_2005mclustcomp
\insertReflancichinetti_detecting_2009mclustcomp
## 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)
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