Functions>Generalappliedmath,StatisticsPerformsk-meansclusteringviatheHartiganandWongAS-136algorithm.Availableinversion6.3.0andlater.functionkmeans_as136(x:numericAS" />
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Performsk-meansclusteringviatheHartiganandWongAS-136algorithm.
Availableinversion6.3.0andlater.
functionkmeans_as136(
x:numericAS136鋼材多少錢一千克,;floatordouble
k[1]:integer,
opt[1]:logical
return_val:floatordouble
Thereturnarray(say),clcnter,willcontainthecluster
centers.Itwillbedimensioned(k,N),whereN
collectivelyrepresentsthe'variable'dimension(s).
clcnterwillhavethefollowingattributesassociatedwithit:
id-aone-dimensionalintegerarrayof
sizeMindicatingtheclustertowhicheachobservationis
assigned.
npts-aone-dimensionalintegerarrayof
sizekcontainingthenumberofpointsineachcluster.
ss2-aone-dimensionaldoublearrayof
sizekcontainingthewithin-clustersumofsquares.
K-meansisacentroid-basedclustermethod.
Theobservationsareallocatedtokclustersinsuchawaythatthe
within-clustersumofsquaresisminimized.K-meansclusteringrequiresthat
thenumberofclusterstobeextractedbespecifiedinadvance.
Asnotedby
"Thenumberofclustersshouldmatchthedata.Anincorrectchoiceofthenumberofclusterswillinvalidatethewholeprocess.AnempiricalwaytofindthebestnumberofclustersistotryK-meansclusteringwithdifferentnumberofclustersandmeasuretheresultingsumofsquares."
Thek-meansalgorithmworksreasonablywellwhenthedatafitstheclustermodel:
Thenumberofclustersis'consistent'withthedata.
Thedatapointswithinaclusterarecenteredaroundthatcluster
Thespread/varianceoftheclustersissimilar,ieeachdatapointbelongstotheclosestcluster
Limitations:K-meansmayhaveproblemswhenclustersareofverydifferingsizes;
outliersarepresent;oremptyclustersexist.
TheoriginalcodeiscreatedforCartesiangrids.Iftheapplicationrequiresusinggridpoints
orstationslocatedathighlatitudes,itissuggestedthat
css2cbeused
tointerpolatetoCartesiancoordinates.Thesebetterreflectthetruedistancesandshouldbeinputtothefunction.
Themodifiedcodeusedbythisfunctionwasdownloadedfrom
JohnBurkardt'swebsite.
TheoriginalHartigan&WongFortrancodewasfrom:
JohnHartigan,ManchekWong,
AlgorithmAS136:
AK-MeansClusteringAlgorithm,
AppliedStatistics,
Volume28,Number1,1979,pages100-108.
Example1:Thesourceofthisexampleis
Defaultoptionsareused.
v0(/1.0AS136鋼材多少錢一千克,1.5,3.0,5.0,3.5,4.5,3.5/);1stvariable
v1(/1.0,2.0,4.0,7.0,5.0,5.0,4.5/);2ndvariable
mdimsizes(v1);#observations
n2;#variables
k2;#clusters(userspecified)
xnew((/n,m/),typeof(v1),"No_FillValue")
x(0,:)v0
x(1AS136鋼材多少錢一千克,:)v1
clcntrkmeans_as136(x,k,False);usedefaultoptions
print(clcntr);(1.25,1.5)and(3.9,5.1)
Aneditedversionoftheoutputfollows:
Variable:clcntr
Type:float
TotalSize:16bytes
4values
NumberofDimensions:2
Dimensionsandsizes:[2]x[2](kcX(:,{-30:30},:);x(time,lat,lon)
;reorderviaNCL'snameddimensionreordering
xrx(lat|:,lon|:,time|:);make'time'(observations;M)therightmostdimension
;thelat,lonarethe'variables'(N)
k3;#clusters(userspecified)
optTrue
opt@iseed1
clcntrkmeans_as136(xr,k,opt);inputthereorderedarray
:;clcntr(3,nlat,mlon)
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