Introduction: the necessity to reduce the complexity Recall: what cluster analysis does
An example : cluster analysis in consumer research on fair trade coffee
Discussion
2
Intro
(…)
“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
What cluster analysis does
Cluster analysis can get you from this: a b c d e f
To this:
What cluster analysis does
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 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.
What cluster analysis does
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 clusterforming variables
3. appellation of the clusters with catchy titles
What cluster analysis does
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
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
