[PDF] SPSS tutorial on cluster analysis (.pdf)





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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: ...

SPSS tutorial on cluster analysis (.pdf)

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 assignment

Cluster 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 mapping

Steps 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 -4

Defining distance: the Defining distance: the

Euclidean distanceEuclidean distance

Dijdistance between cases iand j

xkivalue of variable Xkfor case j

Problems:

•Different measures = different weights •Correlation between variables (double counting)

Solution:Principal component analysis

2 1 n ij ki kj kD x x

Clustering 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 clustering

Agglomerative clusteringAgglomerative clustering

Agglomerative Agglomerative

clusteringclustering •Linkage methods -Single linkage (minimum distance) -Complete linkage (maximum distance) -Average linkage •Ward's method

1.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 seeds

3.Given a certain treshold, all units are assigned to

the nearest cluster seed

4.New seeds are computed

5.Go back to step 3 until no reclassification is

necessary

Units can be reassigned in successive steps

(optimising partioning)

Hierarchical vs Non Hierarchical vs Non

hierarchical methodshierarchical methods

Hierarchical

clustering •No decision about the number of clusters •Problems when data contain a high level of errorquotesdbs_dbs2.pdfusesText_2
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