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For example in this book you'll learn how to compute easily clustering algorithm using the cluster R package There are thousands other R packages available
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Abstract- Cluster analysis divides the data into groups that are meaningful useful or both It is also used as a starting point for other purposes of data
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Cluster Analysis using R Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the
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?An example : cluster analysis in consumer research on fair trade coffee relate objects with a high similarity to the same cluster and objects
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The strength of clustering is indicated by the level of similarity at which elements join a cluster In the example above elements 1-2-3-4 join at similar
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Indeed any investigation into the classification of objects for which taxonomy may be taken as the type example or any investigation into the dispersion of
1 © A. Kassambara 2015 Vultivariate Nnalysis R
Alboukadel Kassambara A-Practical Guide To Cluster Analysis in REdition 1 sthda.com
Unsupervised Machine Learning
2 Copyright©2017by AlboukadelKassambara. Allrightsreserved. PublishedbySTHDA (http://www.sthda.com),AlboukadelKassambara Nopartof thisp ublicationm ayb ereproduced,storedinaretrievalsystem ,ortransmittedinanyform orby anymeans, electronic,mechanical,photocopy ing,recording,scanning,orotherwise,withouttheprior writtenpermission ofthePublisher.Req ueststothe Publisherf orpermissionsh ould bead dressedtoSTHDA(http://www. sthda. com). LimitofLiabilit y/Dis claimerofWarranty:Whilethepublisherandauthorhaveusedtheirbe ste ortsin preparingthisb ook ,theymakenorepresentationsor warrantieswithrespecttotheaccuracyor completenessofthe contents ofthis bookandspecificallydisclaiman yimplied warranties of merchantabilityorfitnessforaparticularpurpose. Now arrant ymaybecreated orextended bysales representativesorwrittensalesmaterials. NeitherthePubli shernor theauthors,contributors,oreditors, assumeany liabilityforan yinjuryand/or damage topersons orpropertyas amatterof productsliability, negligenceorotherwise, orfrom anyuse oroperation ofany methods,products,instructions, orideascontainedin thematerialherein. Forgene ralinformationcontac tAlboukadelKassambara0.1.PREFAC E3
0.1Preface
Largeamountsofdat aarecollectedever ydayfr omsatelliteima ges,bio-medica l, security,marketing,websea rch,geo-spatialorotherauto maticequipment .Mining knowledgefromthesebigdat afarexceedshuma n'sabilities. Clusteringisone oftheimpo rtantda taminingmet hodsfordiscoveringknowledge inmult idimensionaldata.Thegoalofclust eringistoidentifypa tterno rgroupsof similarobject swithinadatasetofinterest . Inthelitt eratur e,itisreferredas"patternrecog nition" or" unsupervisedmachine learning"-"unsupervised" bec ausewearenotguidedbyaprioriideasof which variablesorsamplesbelonginw hichclust ers."Learning"b ecausethemachine algorithm"learns"howtoc luster.Clusteranalysisispopular inmanyfields,including:
•Incancerresearchforclassify ingpatientsintosubgroupsaccor dingtheirgene expressionprofile.Thiscanbeuseful foridentifyingthe molecularpr ofileof patientswithgoodorbadpr ognostic,a swellasforunders tandingthedis ease. •Inmarketingformarketsegmentationbyidentif yingsubgroupsofcustomersw ith similarprofilesand whomightberece ptivetoa particular formofadve rtising. •InCity-planningforidentifying groupsofhousesaccordingt otheirtype,va lue andlocat ion. Thisbook providesapractica lguidetounsupervisedma chinele arningorcluster analysisusingRsoftwar e.Additio nally, wedeveloppedanRpackagenamedfactoextra tocrea te,easily,aggplot2-basede legantplotsofcluster analy sisresults.Factoextra o cialonlinedocumen tation:h ttp://www.sthda.com/english/rpkgs/fact oextra 40.2Aboutthe author
AlboukadelKassambaraisaP hDinBioinformaticsandCancerBiology.Hew or kss ince manyyearso ngenomicdataanalysisa ndvisualiza tion.Hecreateda bioinformatics toolnamedGenomicSc ape(www.ge nomicscape.com)whichisaneasy-to -usewebtool forgeneexpr essiondata analysisandvisualization. Hedev elopedalsoawebsitecalledSTHD A(Statistica lT oolsforHigh-throughputDa ta Analysis,www.sthda.com/e nglish),whichcontainsmanytutorialsondataanalysis andvisualiz ationusingRsoftwareandpackage s. Heist heaut horoft heRpackagessurvminer(foranalyzinganddr awingsurvival curves),ggcorrplot(fordrawingcor relationmatrixusing ggplot2)andfactoextra (toeasilye xtractandvisualiz etheresultsofmultivariateana lysissuch PCA,CA, MCAandcluste ring).Yo ucanlearnmoreaboutthesepacka gesat:ht tp://www. sthda.com/english/wiki/r-packages Recently,hepublishedtwobooksonda tav isualization:1.GuidetoCr eateBe autifulGraphicsinR(at:ht tps://goo.gl/vJ0OYb).
2.CompleteGuideto3DPlots inR(at :https:/ /goo.gl/ v5gwl0).
Contents
0.1Pref ace................. ... ... .. ... ... ... .3
0.2Aboutt heauthor.... ..... ............ ... ... ..4
0.3Keyf eaturesoft hisbook............... ...... ....9
0.4Howthis bookis organize d?........... .. ..........10
0.5Book website....... .............. ... ... ... .16
0.6Exec utingtheRcodesfromthePD F..... .. ...........16
IBa sics17
1Int roductiontoR18
1.1Insta llRandRStudio........ .. ...... ......... .18
1.2Insta llingandloadingRpackages ...... ......... ... ..19
1.3Gett inghelpwithfunctionsinR. ...... ......... .....20
1.4Impor tingyourdataintoR..... ......... ......... 20
1.5Demoda tasets... ........ ............ ... ... .22
1.6Close yourR/RStudioses sion...... .............. ..22
2Da taPreparation andRPackages23
2.1Data preparation... ....................... ... 23
2.2Requir edRPackages...... ... ..................24
3Cl usteringDistanceMeasures25
3.1Metho dsformeasuringdistance s........ ............25
3.2Whatty peofdist ancemeasuressho uldwechoo se?..........27
3.3Data standardization.. .........................28
3.4Dista ncematrixcomputation.... ........... .......29
3.5Visualizing distancematrices. .............. ........32
3.6Summary. ......... ... .. ... ... ... ... ... ... 33
56CONTENTS
IIPart itioningClustering34
4K-M eansClustering36
4.1K-mea nsbasicideas....... ......... ........ ... 36
4.2K-mea nsalgorithm....... .................. ... 37
4.3Computing k-meansclusteringin R............. ......38
4.4K-mea nsclusteringadvantag esanddisadvantages.... .......46
4.5Alterna tivetok-meansclustering.... ...... ..........47
4.6Summary. ........ ... ... ... ... ... .. ... ... .47
5K-M edoids48
5.1PAMco ncept.. ............... ... .. ... ... ... 49
5.2PAMalg orithm.. ................. ... ... ... ..49
5.3Computing PAMinR........ ..... ... ... ... ... .50
5.4Summary. ......... ... .. ... ... ... ... ... ... 56
6CL ARA-ClusteringLar ge Applications57
6.1CLARA concept...... .............. ... ... ... 57
6.2CLARA Algorithm....... .............. ... ... .58
6.3Computing CLARAinR...... ........... ... ... ..58
6.4Summary. ......... ... .. ... ... ... ... ... ... 63
IIIHierar chicalClustering64
7Ag glomerativeClustering67
7.1Algorit hm............. ... ... ... .. ... ... ... 67
7.2Stepst oagglomerat ivehiera rchicalclustering.............68
7.3Verif ytheclustertree.. ...... ............... ....73
7.4Cutthe dendrogra mintodi
erentgroups..... ...........747.5Cluste rRpackage...... ... .................. .. 77
7.6Applicatio nofhierarchicalclust eringtog eneexpressiondataanalysis77
7.7Summary. ......... ... .. ... ... ... ... ... ... 78
8Co mparingDendrograms79
8.1Data preparation.... ....................... ..79
8.2Compar ingdendrograms...... ...................80
9Vi sualizingDendrograms84
9.1Visualizing dendrograms.... .................... .85
9.2Case ofdendrogramwit hlargeda tasets.............. ..90
CONTENTS7
9.3Manipulat ingdendrogramsusingdendext end..............94
9.4Summary. ......... ... .. ... ... ... ... ... ... 96
10Heat map:StaticandInteractive9 7
10.1RPacka ges /functionsfordrawingheatmaps..............97
10.2Datapre paration...... ....................... 98
10.3Rbasehe atma p:heatmap().. .............. .......98
10.4Enhancedhe atmaps:heatmap.2().. ......... ........101
10.5Pretty heatmaps:pheatmap()..... ......... ........102
10.6Inter activeheatmaps:d3heatmap()........... ........103
10.7Enhancinghea tmapsusingdendextend ............... ..103
10.8Complexhea tmap........ ............... ... ... 104
10.9Applicationt ogeneexpressionmat rix.... ......... .....114
10.10Summary.............. .. ... ... ... ... ... ..116
IVCl usterValidation117
11Asse ssingClusteringTendency119
11.1Require dRpackages......... ... ...............119
11.2Datapre paration...... ....................... 120
11.3Visualinsp ectionofthe data................. ...... 120
11.4Whyasse ssingclus teringtendency?.......... .........121
11.5Methodsf orassessingcluster ingtendency... ............123
11.6Summary... ......... .. ... ... ... ... ... ... .127
12Dete rminingtheOptimalNumberofCluster s128
12.1Elbowme thod......... ........... ... ... ... ..129
12.2Averag esilhouettemethod........ ................130
12.3Gapsta tisticmethod. .................... ......130
12.4Computingt henumberofcluste rsusingR. .............. 131
12.5Summary... ......... .. ... ... ... ... ... ... .137
13Clust erValidationStatist ics138
13.1Interna lmeasuresforclustervalidatio n................ .139
13.2Externa lmeasuresforclusteringvalidat ion............... 141
13.3Computingclus tervalidationst atisticsinR....... .......142
13.4Summary... ......... .. ... ... ... ... ... ... .150
14Choos ingtheBestClusteringA lgorit hms151
14.1Measure sforcomparingclusteringalgorit hms........ .....151
8CONTENTS
14.2Comparec lusteringalgorithmsinR. .................. 152
14.3Summary... ......... .. ... ... ... ... ... ... .155
15Comput ingP-valueforHierarc hicalClustering156
15.1Algorithm ............... ... ... .. ... ... ... .156
15.2Required packages.......... ..................157
15.3Datapre paration...... ....................... 157
15.4Computep- valueforhierarchic alclustering... ......... ...158
VAdv ancedClustering161
16Hier archicalK-MeansClustering163
16.1Algorithm. .............. ... ... ... ... ... ... 163
16.2Rcode.. ... ........ ... ... ... ... ... ... ... .164
16.3Summary... ......... .. ... ... ... ... ... ... .166
17FuzzyCl ustering 167
17.1Required Rpackages......... ... ...............167
17.2Computingfuz zyclustering... ............ ........168
17.3Summary... ......... .. ... ... ... ... ... ... .170
18Model -BasedClustering171
18.1Concept ofmodel-basedclustering ...... ..............171
18.2Estimating modelparameters...... ......... .......173
18.3Choosingt hebestmodel...... ...... ............ .173
18.4Computingmo del-basedclustering inR.................173
18.5Visualizingmode l-basedclustering ...................175
19DBSCA N:Density-BasedClust ering177
19.1WhyDBSCAN? ...... ........... ... ... ... ... .178
19.2Algorithm. .............. ... ... ... .. ... ... .180
19.3Advanta ges....................... ... ... ... 181
19.4Parame terestimation.............. .............182
19.5ComputingDB SCAN......... ............ ... ... 182
19.6Methodf ordeterminingtheoptimale psvalue..... ........184
19.7Clusterpr edictionswithDBSCANalg orithm.............. 185
20Refe rencesandFurtherReading186
0.3.KEYFEAT URESOFTHISB OOK9
0.3Keyfea turesofthis book
Althoughthereares everalgoodb ooksonunsupe rvisedmachinelearning/clustering andrelat edtopics,wefeltthatman yofthemareeithert oohigh-lev el,theoretic al ortooa dvanced.Ourgo alwastowriteapractic alguideto clusteranalysis, elegant visualizationandinterpretation.Themainpar tsofthe bookinclude:
•distancemeasures , •partitioningclustering, •hierarchicalclustering, •clustervalidationmetho ds,as wellas, •advancedclusteringmethodssuchasfuzzy clustering ,density-basedc lustering andmodel-ba sedclustering. Thebook presentsthebasic principlesofthesetasksandprov idemanyex amplesinR.Thisbo oko
erssolidguida nceindataminingfo rstudentsandre sea rchers.Keyfeature s:
•Coversclusteringalgorithm andimplementation •Keymathemat icalconceptsarepresented •Short,self-cont ainedchapterswithpracticalexamples.Thismeanstha t,you don'tneedt oreadthedi erentchaptersinseq uence. Attheend ofeac hchapter ,wepres entRlabsectio nsinwhichwesystematically workthroughapplica tionsofthevariousmet hodsdiscussedinthatchapter.10CONTENTS
0.4Howthis bookisorg anized?
Thisbook contains5parts. PartI(Chapter1-3)pro vides aquickintroductionto R(c hapter1)andpresentsre quiredRp ackag esanddataformat(Chapter2)f or clusteringanalysisandvisualiza tion. Theclass ificationofobjects,intoclusters, requires somemethodsformeasuringthe distanceorthe(dis)similar itybet weenthe objects.Chapter3coversthec ommon distancemeasuresused forassessingsimilaritybe tweenobser vations. PartIIstarts withpart itioningclusteringmethods,which include: •K-meansclustering(Chapt er4), •K-MedoidsorPAM(partitioning aroundmedo ids)algor ithm(Chapter5)and •CLARAalgorithms( Chapter6). Partitioningclusteringapproachess ubdividethedatasetsintoas etofkgroups,where kist henum berof groupspre-specifiedby theanaly st.0.4.HOWTHISB OOKISO RGANIZED?11
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IowaKansas
KentuckyLouisiana
MaineMaryland
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-1 0 1 2 -202Dim1 (62%)
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clusteraaaa1234Partitioning Clustering Plot
InPart III,weconsidera gglomerativehier archical clusteringmethod,whichis an alternativeapproachtopartitionningc lusteringforidentifyinggroups inadata set. Itdoesno trequiretopr e-spe cifythenumberofclust erstob egenerated.Theresult ofhierar chicalclusteringisatree-basedre presentationoftheobj ect s,w hichisalso knownasdendrogram(seethefigurebe low). Inthispar t,wede scribehowtocomput e,visualiz e,interpretandcomparede ndro- grams: •Agglomerativeclustering(Chapter7) -Algorithmandsteps -Verifytheclustertr ee -Cutthedendr ogram intodi"erentgroups •Comparedendrograms(Cha pter8) -Visualcomparis onoftwodendrograms -Correlationmatrixbetweenalistofde ndrograms12CONTENTS
•Visualizedendrograms (Chapter9) -Caseofsmalldata sets -Caseofdendrogra mwithlar gedatasets:zoom,sub-tree ,PDF -Customizedendrogramsusingdendex tend •Heatmap:staticandint eractive(Chapter10 ) -Rbas eheatmaps -Prettyheatmaps -Interactiveheatmaps -Complexheatmap -Realapplication:ge neexpressiondata Inthisse ction,yo uwilllearnhowtogenerat eandinterpre tthefollo wingplots. •Standarddendrogramwit hfilledrectanglearoundcluste rs:Alabama
Louisiana
Georgia
Tennessee
North Carolina
Mississippi
South Carolina
TexasIllinois
New York
Florida
Arizona
MichiganMaryland
New Mexico
Alaska
Colorado
California
Nevada
South Dakota
West VirginiaNorth Dakota
Vermont
IdahoMontana
Nebraska
MinnesotaWisconsin
Maine IowaNew Hampshire
Virginia
WyomingArkansasKentuckyDelaware
Massachusetts
New JerseyConnecticut
Rhode Island
Missouri
Oregon
Washington
Oklahoma
IndianaKansas
OhioPennsylvania
Hawaii
Utah 0 5 10Height
Cluster Dendrogram
0.4.HOWTHISB OOKISO RGANIZED?13
•Comparetwodendrograms:3.02.01.0 0.0
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