data science course in pune
                     
PowerPoint Presentation



1
Cluster Analysis:
A practical example


2
Content
 Introduction: the necessity to reduce the 

complexity

Recall: what cluster analysis does
An example : cluster analysis in consumer 

research on fair trade coffee 

Discussion


(…)
“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, 
Choruses from the Rock
(1888 – 1965)

Intro


(…)

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


In order to go from  data to information, to 
knowledge and to wisdom,
we need to reduce the complexity of the data.

Complexity can be reduced on
- case level : cluster analysis
- on variable level: factor analysis

Intro


Cluster analysis can get you from this: 

To this:

What cluster analysis does

a
b

d
e
f

c


Cluster analysis 

• generate groups which are similar

• homogeneous within the group and as much as 
possible heterogeneous to other groups 

• data consists usually of objects or persons

• segmentation based on more than two variables

What cluster analysis does


Cluster analysis 

• generates groups which are similar

• the groups are homogeneous within themselves 
and as much as possible heterogeneous to other 
groups 

• data consists usually of objects or persons

• segmentation is based on more than two 
variables

What cluster analysis does


Examples for datasets used for cluster 
analysis:

• socio-economic criteria: income, education, profession, 
age, number of children, size of city of residence ....

• psychographic criteria: interest, life style, motivation, 
values, involvement

• criteria linked to the buying behaviour: price range, type 
of media used, intensity of use, choice of retail outlet, 
fidelity, buyer/non-buyer, buying intensity

What cluster analysis does


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

objects

• relate objects with a high similarity to the same cluster and objects 
with low similarity to different clusters

• differentiation of nominal-scaled and metric-scaled variables

What cluster analysis does

  m

d(yi,ys) = [∑ |yij-ysj|r]1/r

  j=1

y = vector

i,s = different objects

j = the different characteristics

r = changes the weight of assigned distances
the calculation of the distances measures is the basis of the 
cluster analysis.


Two phases:
1. Forming of clusters by the chosen data set – resulting 

in a new variable that identifies cluster members 
among the cases

2. Description of clusters by re-crossing with the data

What cluster analysis does


Cluster Algorithm in agglomerative hierarchical 
clustering methods – seven steps to get clusters

1. each object is a independent cluster, n

2. two clusters with the lowest distance are merged to 
one cluster. reduce the number of clusters by 1 (n-1)

3. calculate the the distance matrix between the new 
cluster and all remaining clusters

4. repeat step 2 and 3, (n-1) times until all objects form 
one reminding cluster

What cluster analysis does


Finally…

1. decide upon the number of clusters you want to keep 
(decision often based on the size of the clusters)

2. description of the clusters by means of the cluster-
forming variables

3. appellation of the clusters with catchy titles

What cluster analysis does


What cluster analysis does

Cluster 5Cluster 4Cluster 3Cluster 2Cluster 1


 Practical Example

Consumers and Fair Trade Coffee (1997!)

214 interviews of consumers of fair trade 
coffee (personal and telephone interviews)

Cluster analysis in order to identify consumer 
typologies

Identification of 6 clusters 
Description of these clusters by further 

analysis: comparison of means, crosstabs etc.


 Consumers and Fair Trade Coffee 

Description of clusters:

Cluster 1 (11,6%): “self-oriented fair trade buyer”
Cluster 2 (13,6%): “less ready to take personal 

constraints”
Cluster 3 (18,2%): ”less engaged about fair trade”
Cluster 4 (32,2%): “intensive buyer”
Cluster 5 (18,7%): “value-oriented”
Cluster 6 (5,6%): “does not like the taste of fair trade 

coffee”


 Consumers and Fair Trade Coffee 

Description of Cluster 1 (11,6%): “self-oriented fair trade 
buyer” :

 
Searches satisfaction by doing the good thing
 Is not altruistic
Buys occasionally
Sticks to his conventional coffee brand
High level of formal education
Frequently religious (catholic or protestant)


 Consumers and Fair Trade Coffee 

Description of Cluster 2 (13,6%): “less ready to take 
personal constraints”

States that “fair trade coffee is hard to find”
Feels responsible for fare development issues
Believes that fair trade is efficient for developing 

countries
 Is less ready to go to special fair trade outlets
Buys conventional coffee
Likes the taste of fair trade coffee


 Consumers and Fair Trade Coffee 

Description of clusters Cluster 3 (18,2%): ”less engaged 
about fair trade” :

Feels no personal responsibility with regard to 
development questions

Doesn’t see the efficiency of the consumption of fair 
trade goods

The only thing that can make him change is the 
influence of friends

 Is older then the average fair trade buyer and has less 
formal education


 Consumers and Fair Trade Coffee 

Description of clusters: Cluster 4 (32,2%): “intensive 
buyer”

Has abandoned conventional coffee brands
Has started to buy fair trade quite a while ago (> 3 

years)
Shops frequently in fair trade stores (and not in organic 

retail)
 Is ready to act for fair development and talks to friends 

about it
Relatively young, with low incomes and high 

educational values


 Consumers and Fair Trade Coffee 

Description of clusters: Cluster 5 (18,7%): “value-
oriented”

Together with cluster 4 highly aware of development 
issues

Ready to act and to constraint consumption habits
Buys for altruistic reasons 
Highly involved in social / political action
Most frequently women, highest household income 

among all clusters
Own security is the basis for solidary action


 Consumers and Fair Trade Coffee 

Description of clusters: Cluster 6 (5,6%): “does not like 
the taste of fair trade coffee”

Lowest purchase intensity of all clusters
Not willing to accept constraints in consumption habits 

or higher prices
Most members of these group are attached to a 

conventional coffee brand
Relatively high incomes, age within the average of all 

groups, lower level of formal education
Less religious than other groups.


Conclusion / 
discussion

Advantages

• no special scales of measurement necessary

• high persuasiveness and good assignment to realisable 
recommendations in practice

Disadvantages

• choice of cluster-forming variables often not based on 
theory but at random

• determination of the right number of clusters often time-
consuming – often decided upon arbitrarily

• high influence on the interpretation of the scientist, difficult 
to control (good documentation is needed)


Conclusion / 
discussion


Russell .L. Ackoff, "From Data to Wisdom," Journal of Applied Systems 
Analysis 16 (1989): 3-9. 

Milan Zeleny, "Management Support Systems: Towards Integrated 
Knowledge Management," Human Systems Management 7, no 1 
(1987): 59-70. 

Tashakkori, A. and Ch. Teddlie: Combining Qualitative and Quantitaive 
Approaches. Applied Social Research Methods Series, Volume 46. 
Thousand Oaks, London, New Delhi, 1998.

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