[PDF] [PDF] SPSS Tutorial-Cluster Analysis

[PDF] Cluster Analysis on SPSS - East Carolina University

17 jan 2016 · I have never had research data for which cluster analysis was a technique page, then other online sources were used – for example, from the 

[PDF] SPSS Tutorial-Cluster Analysis

Steps to conduct a Cluster Analysis 1 Select a distance measure 2 Select a clustering algorithm 3 Determine the number of clusters 4 Validate the analysis  

[PDF] Clustering Example

Clustering Example The purpose of Analyze → Classify → Hierarchical Clustering Open the You can tell SPSS to work with transformed values I prefer to 

[PDF] Research Methodology: Tools - schwarz & partners GmbH

Cluster Analysis with SPSS: A Detailed Example SPSS: Analyze>Classify> Hierarchical Cluster You will be able to perform a cluster analysis with SPSS

[PDF] Cluster Analysis Tutorial - ResearchGate

Know the use of hierarchical clustering and K-means cluster analysis • Know how to use cluster analysis in SPSS • Learn to interpret various outputs of cluster  

[PDF] Tutorial SPSS Hierarchical Cluster Analysis - Arif Kamar Bafadal

This table shows how the cases are clustered together at each stage of the cluster analysis Page 5 Tutorial Hierarchical Cluster - 5 Clusters are formed by  

[PDF] Cluster Analysis

SPSS has three different procedures that can be used to cluster data: As an example of agglomerative hierarchical clustering, you'll look at the judging of

[PDF] Cluster analysis – with SPSS

Bus Adm 216: Cluster Analysis Using SPSS Start with an existing data file marketvalues sav Dataset screen shot Next step: Click on analyze >> Classify 

[PDF] Cluster Analysis - Discovering Statistics

5 fév 2000 · The example used by Field (2000) was a questionnaire measuring ability on an SPSS exam, and the result of the factor analysis was to isolate 

[PDF] Cluster analysis with SPSS

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 First  

[PDF] cluster analysis example marketing

[PDF] cluster analysis example pdf

[PDF] cluster analysis example ppt

[PDF] cluster analysis pdf free

[PDF] cluster analysis spss pdf

[PDF] cluster analysis tutorial pdf

[PDF] cluster analysis: basic concepts and algorithms

[PDF] clustering algorithm unknown number of clusters

[PDF] clustering algorithms for bank customer segmentation

[PDF] clustering in data mining lecture notes

[PDF] clustering pdf

[PDF] cm s 1 to m s 1

[PDF] cm to m

[PDF] cm to mm

[PDF] cm to nm

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