9 Cluster_Analysis Schaer

8/20/2019 9 Cluster_Analysis Schaer

1/25

1

Cluster Analysis:

A practical example


2/25

2

Content

Introduction: the necessity to reduce thecomplexity

Recall: what cluster analysis doesAn example : cluster analysis in consumer

research on fair trade coffee

Discussion


3/25

(…)

“Where is the life, we have lost in living?

Where is the wisdom, we have lost in knowledge?

Where is the knowledge, we have lost in information?”

(…)

T. S. Elliot

!horuses from the Roc"

#1$$$ % 1&'()

Intro


4/25

(…)

Where is the wisdom, we have lost in knowledge?

Where is the knowledge, we have lost in information?

(…)

“Where is the information we have lost in data?”

Intro


5/25

In order to *o from data to information to

knowledge and to wisdom

we need to reduce the complexity of the data.

!omplexity can +e reduced on

, case le-el : cluster analysis

, on -aria+le le-el: factor analysis

Intro


6/25

Cluster analysis can get you from this:

To this:

What cluster analysis does

a b

d

ef

c


7/25

!luster analysis

*enerate *roups which are similar

homo*eneous within the *roup and as much aspossi+le hetero*eneous to other *roups

data consists usually of o+/ects or persons

se*mentation +ased on more than two -aria+les



8/25

Cluster analysis

*enerates *roups which are similar

the *roups are homo*eneous within themsel-esand as much as possi+le hetero*eneous to other

*roups

data consists usually of o+/ects or persons se*mentation is +ased on more than two

-aria+les



9/25

Examples for datasets used for clusteranalysis:

socio,economic criteria: income education profession

a*e num+er of children si0e of city of residence ....

psycho*raphic criteria: interest life style moti-ation

-alues in-ol-ement

criteria lin"ed to the +uyin* +eha-iour: price ran*e type

of media used intensity of use choice of retail outlet

fidelity +uyernon,+uyer +uyin* intensity



10/25

Proximity Measures roximity measures are used to represent the nearness of two

o+/ects

relate o+/ects with a hi*h similarity to the same cluster and o+/ects

with low similarity to different clusters

differentiation of nominal,scaled and metric,scaled -aria+les


m

d#yiys) 3 45 6yi/,ys/6r71r

/31

y 3 -ector

is 3 different o+/ects

/ 3 the different characteristics

r 3 chan*es the wei*ht of assi*ned distancesthe calculation of the distances measures is the +asis of the

cluster analysis.


11/25

Two phases:

1. 8ormin* of clusters +y the chosen data set % resultin*

in a new -aria+le that identifies cluster mem+ers

amon* the cases

2. Description of clusters +y re,crossin* with the data



12/25

Cluster Algorithm in a**lomerati-e hierarchicalclusterin* methods % se-en steps to *et clusters

1. each o+/ect is a independent cluster n

2. two clusters with the lowest distance are mer*ed to

one cluster. reduce the num+er of clusters +y 1 #n,1)

9. calculate the the distance matrix +etween the new

cluster and all remainin* clusters

. repeat step 2 and 9 #n,1) times until all o+/ects form

one remindin* cluster



13/25

Finally

1. decide upon the num+er of clusters you want to "eep

#decision often +ased on the si0e of the clusters)

2. description of the clusters +y means of the cluster,

formin* -aria+les

9. appellation of the clusters with catchy titles



14/25


!luster (!luster !luster 9!luster 2!luster 1


15/25

Practical Example

!onsumers and 8air Trade !offee #1&&;


16/25

Consumers and Fair Trade Coffee

!escription of clusters:

!luster 1 #11'=): >self,oriented fair trade +uyer?

!luster 2 #19'=): >less ready to ta"e personalconstraints?

!luster 9 #1$2=): ?less en*a*ed a+out fair trade?

!luster #922=): >intensi-e +uyer?

!luster ( #1$;=): >-alue,oriented?!luster ' #('=): >does not li"e the taste of fair trade

coffee?


17/25


!escription of Cluster " #""$%&': (self)oriented fair trade

buyer* :

Searches satisfaction +y doin* the *ood thin* Is not altruistic

@uys occasionally

Stic"s to his con-entional coffee +rand

i*h le-el of formal education8reBuently reli*ious #catholic or protestant)


18/25


!escription of Cluster + #",$%&': (less ready to take

personal constraints*

States that >fair trade coffee is hard to find?8eels responsi+le for fare de-elopment issues

@elie-es that fair trade is efficient for de-elopin*

countries

Is less ready to *o to special fair trade outlets@uys con-entional coffee

Ci"es the taste of fair trade coffee


19/25


!escription of clusters Cluster , #"-$+&': *less engaged

about fair trade* :

8eels no personal responsi+ility with re*ard tode-elopment Buestions

Doesnt see the efficiency of the consumption of fair

trade *oods

The only thin* that can ma"e him chan*e is theinfluence of friends

Is older then the a-era*e fair trade +uyer and has less

formal education


20/25


!escription of clusters: Cluster . #,+$+&': (intensi/e

buyer*

as a+andoned con-entional coffee +rands

as started to +uy fair trade Buite a while a*o # 9

years)

Shops freBuently in fair trade stores #and not in or*anic

retail) Is ready to act for fair de-elopment and tal"s to friends

a+out it

Relati-ely youn* with low incomes and hi*h

educational -alues


21/25


!escription of clusters: Cluster 0 #"-$1&': (/alue)

oriented*

To*ether with cluster hi*hly aware of de-elopmentissues

Ready to act and to constraint consumption ha+its

@uys for altruistic reasons

i*hly in-ol-ed in social political actionFost freBuently women hi*hest household income

amon* all clusters

Gwn security is the +asis for solidary action


22/25


!escription of clusters: Cluster % #0$%&': (does not like

the taste of fair trade coffee*

Cowest purchase intensity of all clustersHot willin* to accept constraints in consumption ha+its

or hi*her prices

Fost mem+ers of these *roup are attached to a

con-entional coffee +randRelati-ely hi*h incomes a*e within the a-era*e of all

*roups lower le-el of formal education

Cess reli*ious than other *roups.

Conclusion /


23/25

Conclusion /discussion

Ad/antages

no special scales of measurement necessary

hi*h persuasi-eness and *ood assi*nment to realisa+le

recommendations in practice

!isad/antages

choice of cluster,formin* -aria+les often not +ased on

theory +ut at random

determination of the ri*ht num+er of clusters often time,consumin* % often decided upon ar+itrarily

hi*h influence on the interpretation of the scientist difficult

to control #*ood documentation is needed)

Conclusion /


24/25

Conclusion /discussion


25/25

Russell .L. Akoff, !"rom #ata to Wisdom,! $ournal of A%%lied &'stems Anal'sis (*+*) -*.

/ilan 0elen', !/anagement &u%%ort &'stems 1owards 2ntegrated

3nowledge /anagement,! 4uman &'stems /anagement 5, no

(*+5) 6*57.

Tasha""ori A. and !h. Teddlie: !om+inin* ualitati-e and uantitai-e

Approaches. Applied Social Research Fethods Series Jolume '.Thousand Ga"s Condon Hew Delhi 1&&$.

xyxy

Sources

9 Cluster_Analysis Schaer

Documents

Transcript of 9 Cluster_Analysis Schaer