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12 Chapter 15: Cluster analysis There are many other clustering methods For example, a hierarchical di- visive method follows the reverse procedure in that it

For example, clustering has been used to find groups of genes that have hierarchical clustering can be viewed as a sequence of partitional clusterings

[PDF] Chapter 15 Cluster analysis

12 Chapter 15: Cluster analysis There are many other clustering methods For example, a hierarchical di- visive method follows the reverse procedure in that it

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Know the use of hierarchical clustering and K-means cluster analysis • Know how to use cluster analysis in SPSS • Learn to interpret various outputs of cluster

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The agglomerative hierarchical clustering algorithms available in this program module This section presents an example of how to run a cluster analysis of the

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Hierarchical cluster analysis We can also say that cluster analysis enables to: Example #HAC - single linkage cah

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[PDF] Hierarchical clustering - François Husson

Introduction Principles of hierarchical clustering Example K-means Extras Describing the classes Combining with factor analysis - clustering 6 Describing

[PDF] Multivariate Statistics: Hierarchical and k-means cluster analysis

Figure: Example of a hierarchical tree structure Summer term 2017 19/43 Page 20 Proximity measures

Chapter15

Clusteranalysis

15.1INTRODUCTIONANDSUMMARY

thenumberandcompositionofthegroups.

15.2ANEXAMPLE

¯vepersonsareshowninTable15.1.

Table15.1

Illustrativedata,

Example15.1PersonX1X2a24

b82 c93 d15

Figure15.1

Groupingofobservations,Example15.1

clustersarequitedistinctfromeachother.

15.3Measuresofdistanceforvariables3

15.3MEASURESOFDISTANCEFORVARIABLES

Figure15.2

Distancemeasuresillustrated

triangleABC:

D(i;j)=pA

2+B2=q(X1i¡X1j)2+(X2i¡X2j)2:

4Chapter15:Clusteranalysis

vationkifD(i;j)D(i;j)=qw

15.4CLUSTERINGMETHODS

tionsproceedsinthefollowingsteps.

15.4Clusteringmethods5

Figure15.3

isshowninFigure15.4(a).

Figure15.4

Nearestneighbormethod,Step1

15.4(b),aregroupedinthesamecluster.

6Chapter15:Clusteranalysis

example,

Figure15.5

Nearestneighbormethod,Step2

inFigure15.5(b).

Thedistancebetween(be)and(ad)is

whilethatbetweencand(ad)is cluster(bce)showninFigure15.6(b).

15.4Clusteringmethods7

Figure15.6

Nearestneighbormethod,Step3

Figure15.7

Nearestneighbormethod,Step4

8Chapter15:Clusteranalysis

Figure15.8

Nearestneighbormethod,dendrogram

Figure15.9

Clusterdistance,furthestneighbormethod

15.4Clusteringmethods9

clustersareshowninFigure15.10(a).

Figure15.10

Furthestneighbormethod,Step2

10Chapter15:Clusteranalysis

Figure15.11

Twoclusterpatterns

thesecondclusterasshowninFigure15.12.

Figure15.12

Clusterdistance,averagelinkagemethod

thoseofearliermethods.

15.4Clusteringmethods11

Figure15.13

SPSSoutput,averagelinkagemethod

12Chapter15:Clusteranalysis

themembersofeachcluster. oftheclustertowhichtheybelong. calculatedasshowninFigure15.14(a).

D(a;ce)=p(2¡8:75)2+(4¡2)2=7:040:

assigned.Therefore,aisnotreassigned. andRousseeuw(1990).

15.4Clusteringmethods13

Figure15.14

k-meansmethod,Step1

Figure15.15

k-meansmethod,Step2 inFigure15.15(a).

14Chapter15:Clusteranalysis

indicatesthenearestcentroid):

Distancefrom

Obs.Cluster1Cluster2a0.707*6.801

b6.9640.500* c7.6491.118* d0.707*8.078 e7.8261.000* thek-meansmethod.

15.5DISTANCEMEASURESFORATTRIBUTES

bMarriedMale cOtherMale dSingleFemale however,weuseinstead D

15.5Distancemeasuresforattributes15

Obs.abcda0110

b00.51 c01 d0 usualfashion. maritalstatus,gender,andage:

MaritalAgeAge

bMarriedMale30M cOtherMale60O dSingleFemale32M betweenbandc,forexample,is D a(b;c)=1¡13 =23

Obs.abcda0111/3

b02/32/3 c01 d0 andjwasde¯nedas

D(i;j)=K

X k=1jVik¡Vjkj: anda¯ctitioushistoryoffourorders:

OrderItem1,Item2,

no.,kV1kV2kjV1k¡V2kj1110 2011
3101
40002

Jan.1994,pp.101-3.

15.7Tosumup17

questionsinthecluster.

RespondentQ1Q2Q3a105.03.00

b307.53.10 c206.02.90 d408.02.95

VariableQ1Q2Q3Q100.0160.924

Q200.770

Q30

15.7TOSUMUP

clusters. tributes.Othermeasuresarepossible.

18Chapter15:Clusteranalysis

programs).

PROBLEMS

15.2. table:Obs.X1X2a32 b41 c25 d52 e16 f42 saythereare,andwhataretheirmembers? andtheirmembership. table:Obs.X1X2a¡1¡2 b00 c22 d¡2¡2 e1¡1 f12 saythereare,andwhataretheirmembers?

Problems19

andtheirmembership.

Table15.2

B59892418

C55090436

D50090429

E63090415

F5808755

G46087515

H60088429

I59088315

J59989323

K59885223

L61884212

M60088346

N60082329

O60085236

P50083245

Q53980123

R56986121

S68079236

loudmusic. groups.Howwouldyouadvisethemagazine?

20Chapter15:Clusteranalysis

Table15.3

2361211111

3372221123...........................

421294344444

nearestinteger. qualityandprice?

Table15.4.

ofthesewereassignedtocluster1.

Thesixclusterswerelabeledasfollows:

ClusterLabel1Inactives

2Activerecreationists/nonhunters

3Campers

4Passiverecreationists

5Strictlyfallcolorviewers

6Activerecreationists/hunters

Problems21

Table15.4

Deerhunting0.050.080.030.040.020.000.58

Fishing0.130.140.550.060.010.010.54

Canoeing0.050.000.280.040.010.000.30

Sailing0.020.010.040.050.010.000.12

Tennis0.010.000.030.000.020.000.12

Swimming0.070.030.090.130.110.000.15

Scubadiving0.010.020.000.030.000.000.00

Gol¯ng0.040.010.030.000.030.120.15

Table15.5.*

¯lling,watchweight.

22Chapter15:Clusteranalysis

Table15.5

TorelaxTocelebratesomething

AsatreatformyselfIwantalowalcoholdrink

IwantamildtastingdrinkIwantanaturaldrink

IwantafamiliardrinkIwantahealthydrink

IaminnohurryToberomantic

TofeelgoodTobedistinctive

IwantsomethinglightTohelpmesleep

SomethingspecialtoshareTobestylish

TobesociableTowatchmyweight

TosatisfyathirstIfeeldepressed

TohavefunIfeellonely

andnumberofvideogamespurchased.*

Videophiles(8%),andCDBuyers(6%).

fast-paced,consumptionlifestyle". children(p.55)". ics,July1992,pp.48-55.

Problems23

onthebasisofthistypeofstudy.quotesdbs_dbs17.pdfusesText_23

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