Cluster analysis using spss

Hierarchical & K-means cluster analysis in SPSS

1. MULTIVARIATE
ANALYSIS
-Dr Nisha Arora

2. About Me Concepts
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Q/A Session
Agenda

3. •Dr. Nisha Arora is a proficient educator, passionate trainer,
You Tuber, occasional writer, and a learner forever.
✓ PhD in Mathematics.
✓ Works in the area of Data Science, Statistical
Research, Data Visualization & Storytelling
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3
About Me
An educator by heart & a
trainer by profession.

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[email protected] .

7. Cluster Analysis
USING SPSS

8. Applications
✓Clusters of covid active cases
✓Assign projects to different teams of students where each
team member have similar interest
✓Customer segmentation
✓Market Basket Analysis

9. Clustering Evaluations
✓Within group variation should be less
✓Between group variation should be more

10. Clustering Evaluations

11. Clustering Algorithms
Clustering
Techniques
Hierarchical
Divisive
Agglomerative
Partitional
Centroid
Model Based
Graph
Theoretic
Spectral
Bayesian
Decision Based
Non-
parametric

12. Clustering Algorithms

13. Available Options
Analyze -> Classify ->
✓Hierarchical cluster
✓K-means cluster
✓TwoStepcluster
✓Cluster Silhouttes

14. Hierarchical clustering

15. Hierarchical clustering

16. Hierarchical clustering

17. Hierarchical clustering_ Outputs

18. Hierarchical clustering_ Outputs
Proximity Matrix
It gives the distances or similarities
between items.
✓Double Click
✓Pivot

19. Hierarchical clustering_ Outputs
Agglomerationschedule
Itdisplaysthecasesorclusters
combinedateachstage,the
distancesbetweenthecasesor
clustersbeingcombined,andthe
lastclusterlevelatwhichacase
(orvariable)joinedthecluster.

20. Hierarchical clustering_ Outputs
Icicle
✓It displays an
icicle plot,
including all
clusters or a
specified
range of
clusters.
✓It displays
information
about how
cases are
combined into
clusters at
each iteration
of the
analysis.

21. Hierarchical clustering_ Outputs
Icicle
✓Double
Click
✓Options
✓Y axis
reference
line
✓Position –
10
✓Apply

22. Hierarchical clustering_ Outputs

24. Hierarchical clustering_ Outputs
Dendrogramscanbeusedtoassessthecohesivenessoftheclustersformedandcanprovideinformationabout
theappropriatenumberofclusterstokeep.
Possible Clusters –2/3/6/…
Cluster Sizes ?

25. Hierarchical clustering
Let’s change the number of
possible solutions

26. Hierarchical clustering _ Output
We get additional output
as cluster membership

27. Hierarchical clustering
Let’s change the icicles for
specified range of clusters

28. Hierarchical clustering _ Output
Let’s change the icicles for
specified range of clusters

29. Hierarchical clustering_ Outputs
✓ClusterMembership
✓We can save cluster
membershipsforasingle
solutionorarangeof
solutions.
✓Savedvariablescanthenbe
usedinsubsequentanalyses
toexploreotherdifferences
betweengroups.

30. Understanding the clusters
Cross Tab between rank and cluster
membership
We need to give suitable names to the
clusters.

31. Understanding the clusters
We need to give suitable names to
the clusters.
We can do it in variable view
Let’s give the names:
Cluster 1: Seniors
Cluster 3: Adjuncts
Cluster 2: Others

32. Understanding the clusters
We need to give suitable
names to the clusters.
We can do it in variable view
Let’s give the names:
Cluster 1: Seniors
Cluster 3: Adjuncts
Cluster 2: Others

33. Understanding theclustering

34. Understanding the clustering
Althoughcellcountistoolow&chi-
squarestatisticsisnotreliable,still
weseethere’snoassociationbetween
sex&clustermembershipprima
facie.

35. ValidatingHierarchical Clustering
Doubleclick‘AgglomerativeSchedule’
table→Select‘Coefficients’→Rightclick
→CreateGraph→Line
Lookattheplot(likescreeplotinfactor
analysis)→Elbowshouldbeformed
Findstagenumberwhereelbowisformed
Numberofclusters=Totalcases–stage
numberwhereelbowisformed

36. K-means clustering
1. Need to predefine
the number of cluster
2. Solution depends on
initial cluster center
3. Not all patterns can
be segmented
4. Bases on Euclidean
distance
1. Fast (Linear time
complexity)
2. Easy to understand
3. Most popular

37. K-means clustering
Number of Cluster:
Ideally between 2 to 5
[Subjective]
Number of iteration:
10/20 should be enough

38. K-means clustering
We can save cluster
membership.

39. K-means clustering
In ‘Statistics’sub-
dialogbox:
Initial cluster center:
Randomlychosen

40. K-means clustering _Output

41. K-means clustering _Output

42. K-means clustering _Output

43. K-means clustering _Output

44. K-means clustering _Output
We get almost similar cluster membership
Actually, we should first standardize scores
Also, k-means works on Euclidean distance

45. To validate K-means clustering
Analyze →Compare Means →Take all variables
used for clustering in ‘Dependent List’
And cluster membership in ‘Factor’ →
Run ‘Bonferroni or Tukey post hoc test →
See if all p-values are less than level of
significant (0.05)

46. How to standardize variables
Analyze →Select variables →Check
‘Save standardized values as variables’
→Click ‘OK’

47. How to convert string variables to
categorical
Transform →Automatic Recode →
Double-click variable State in the left
column to move it to the Variable →
‘New Name box’: Enter a name for the
new, recoded variable in the New Name
field →click ‘Add New Name’
Check the box for Treat blank string
values as user-missing.
Click OK to finish

48. How to add ID column to data
Transform →Compute Variable →
Give a name to ‘Target variable,
say, ‘ID’→Type ‘$CASENUM’ in
Numeric Expression box (Or double
click on $Casenumfunction from
Functions & Special Variables
menu) →click ‘OK’

49. Thank You