Cluster Analysis on SPSS
17-Jan-2016 Psychology. If the faculty member did not have employment information on his or her web page then other online sources were used – for example
Cluster Analysis on SPSS
17-Jan-2016 Psychology. If the faculty member did not have employment information on his or her web page then other online sources were used – for example
SPSS tutorial on cluster analysis (.pdf)
Cluster analysis. • Example of cluster analysis. • Work on the assignment. Page 3. Cluster Analysis. • It is a class of techniques used to classify cases into
Clustering Example
Clustering Example. The purpose of the analysis was to look for "sub-populations" of adult females You can tell SPSS to work with transformed values. I.
Cluster analysis with SPSS: K-Means Cluster Analysis
Let us briefly go through the different stages of K-Means Cluster Analysis using the data from the example with UniCredit Bulbank (Table 1 from the chapter
IBM SPSS Statistics 19 Statistical Procedures Companion
The goal of cluster analysis is to identify the actual groups. For example if you are interested in distinguishing between several disease groups using
Decision Making Procedure: Applications of IBM SPSS Cluster
IBM SPSS Cluster Analysis and Decision Tree spreadsheets for simple analysis sometimes not clusters” technique is the most applicable for the example.
Analysing data using SPSS
lend themselves to different types of analysis. In the example above we had two variables car age and car colour
Cluster Analysis Using SPSS Start with an existing data file.
Next: We can identify from the SPSS output that the cluster quality is good. Next: Then click on Graphs and then select Chart Builder. Select. Scatter / Dot
Introduction to Cluster Analysis with SPSS Creating Clusters Cluster
22-Feb-2020 Cluster Analysis is a way of grouping cases of data based on the similarity of responses to several variables. There are two types of measure: ...
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SPSS TutorialSPSS Tutorial
AEB 37 / AE 802
Marketing Research Methods
Week 7
Cluster analysis Cluster analysis
Lecture / Tutorial outline
•Cluster analysis •Example of cluster analysis •Work on the assignmentCluster AnalysisCluster Analysis
•It is a class of techniques used to classify cases into groups that are relatively homogeneous within themselves and heterogeneous between each other, on the basis of a defined set of variables. These groups are called clusters.Cluster Analysis and Cluster Analysis and
marketing researchmarketing research •Market segmentation. E.g. clustering of consumers according to their attribute preferences •Understanding buyers behaviours.Consumers with similar
behaviours/characteristics are clustered •Identifying new product opportunities.Clusters of similar brands/products can help
identifying competitors / market opportunities •Reducing data. E.g. in preference mappingSteps to conduct a Steps to conduct a
Cluster AnalysisCluster Analysis
1.Select a distance measure
2.Select a clustering algorithm
3.Determine the number of clusters
4.Validate the analysis
REGR factor score 2 for analysis 1
43210-1-2-3REGR factor score 1 for analysis 1
3 2 1 0 -1 -2 -3 -4Defining distance: the Defining distance: the
Euclidean distanceEuclidean distance
Dijdistance between cases iand j
xkivalue of variable Xkfor case jProblems:
•Different measures = different weights •Correlation between variables (double counting)Solution:Principal component analysis
2 1 n ij ki kj kD x xClustering proceduresClustering procedures
•Hierarchical procedures -Agglomerative (start from nclusters, to get to 1cluster) -Divisive (start from 1cluster, to get to ncluster) •Non hierarchical procedures -K-means clusteringAgglomerative clusteringAgglomerative clustering
Agglomerative Agglomerative
clusteringclustering •Linkage methods -Single linkage (minimum distance) -Complete linkage (maximum distance) -Average linkage •Ward's method1.Compute sum of squared distances within clusters
2.Aggregate clusters with the minimum increase in the
overall sum of squares •Centroid method -The distance between two clusters is defined as the difference between the centroids (cluster averages)KK--means clusteringmeans clustering
1.The number kof cluster is fixed
2.An initial set of k"seeds"(aggregation centres) is
provided •First kelements •Other seeds3.Given a certain treshold, all units are assigned to
the nearest cluster seed4.New seeds are computed
5.Go back to step 3 until no reclassification is
necessaryUnits can be reassigned in successive steps
(optimising partioning)Hierarchical vs Non Hierarchical vs Non
hierarchical methodshierarchical methodsHierarchical
clustering •No decision about the number of clusters •Problems when data contain a high level of errorquotesdbs_dbs2.pdfusesText_2[PDF] cluster analysis example ppt
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