Example of cluster analysis • Work on the assignment Cluster Analysis and marketing research directory (if you saved it there last time Or download it from :
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[PDF] Cluster Analysis - Computer Science & Engineering User Home Pages
Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both data mining There have been many applications of cluster analysis to practical prob- lems http://www cse msu edu/~jain/Clustering Jain Dubes pdf
[PDF] Chapter 15 Cluster analysis
There are a number of clustering methods One method, for example, begins with as many groups as there are observations, and then systemati- cally merges
[PDF] CLUSTER ANALYSIS
inappropriate variables ▻ Non Hierarchical Cluster Analysis can analyze extremely large data sets Page 47
[PDF] Cluster Analysis - ITN
Cluster Analysis: Basic Concepts and Algorithms TNM033: Introduction to Data Mining 1 ➢ What does it mean clustering? ▫ Applications ➢ Types of
[PDF] Cluster Analysis
Once in a cluster, always in that cluster To form clusters using a hierarchical cluster analysis, you must select: ▫ A criterion for determining similarity or distance
[PDF] Cluster Analysis of Medical Research Data using R - CORE
This paper discuss some very basic algorithms like K-means, Fuzzy C-means, Hierarchical clustering to come up with clusters, and use R data mining tool The
[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] Finding Groups in Data: An Introduction to Cluster Analysis
1 MOTIVATION Cluster analysis is the art of finding groups in data To see what is meant by this, let us look at Figure 1 It is a plot of eight objects, on which two
[PDF] SPSS tutorial on cluster analysis (pdf)
Example of cluster analysis • Work on the assignment Cluster Analysis and marketing research directory (if you saved it there last time Or download it from :
pdf Practical Guide To Cluster Analysis in R - Datanovia
Cluster analysis is popular in many fields including: In cancer research for classifying patients into subgroups according their gene expression profile This can be useful for identifying the molecular profile of patients with good or bad prognostic as well as for understanding the disease
Cluster Analysis: Basic Concepts and Algorithms
Cluster analysis can also be used to detect patterns in the spatial or temporal distribution of a disease • Business Businesses collect large amounts of information on current and potential customers Clustering can be used to segment customers into a small number of groups for additional analysis and marketing activities
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Cluster analysis embraces a variety of techniques the main objective of which is to group observations or variables into homogeneous and distinct clusters A simple numerical example will help explain these objectives °Peter c Tryfos 1997 This version printed: 14-3-2001
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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 error •Can be very slow •Initial decision are more influential (one- step only)Non hierarchical
clustering •Faster, more reliable •Need to specify the number of clusters (arbitrary) •Need to set the initial seeds (arbitrary)Suggested approachSuggested approach
1.First perform a hierarchical
method to define the number of clusters2.Then use the k-means procedure
to actually form the clustersDefining the number of Defining the number of
clusters: elbow rule (1)clusters: elbow rule (1)Agglomeration Schedule
47.015004
610.708005
89.974004
481.042136
161.100027
453.680407
143.492568
1116.744709
128.2768010
1128.7879011
1311.4031000
Stage 1 2 3 4 5 6 7 8 9 10 11Cluster 1Cluster 2
Cluster Combined
CoefficientsCluster 1Cluster 2
Stage Cluster First
Appears
Next Stage
StageNumber of clusters
012 111210
39
48
57
66
75
84
93
102
111
n
Elbow rule (2): the Elbow rule (2): the
screescreediagramdiagram 0 2 4 6 8 10 121110987654321
Number of clusters
Distance
Validating the Validating the
analysisanalysis •Impact of initial seeds / order of cases •Impact of the selected method •Consider the relevance of the chosen set of variablesSPSS ExampleSPSS Example
Component1
2.01.51.0.50.0-.5-1.0-1.5Component2
1.5 1.0 .5 0.0 -.5 -1.0 -1.5 -2.0 LUCY JULIA FREDARTHUR
JENNIFER
THOMAS
MATTHEW
NICOLE
PAMELA
JOHNAgglomeration Schedule
36.026008
25.078007
49.224005
17.409006
410.849308
181.456407
124.503629
349.878159
1318.000780
Stage 1 2 3 4 5 6 7 8 9Cluster 1Cluster 2
Cluster Combined
CoefficientsCluster 1Cluster 2
Stage Cluster First
Appears
Next Stage
Number of clusters: 10 -6 = 4
Component1
2.01.51.0.50.0-.5-1.0-1.5
Component2
1.5 1.0 .5 0.0 -.5 -1.0 -1.5 -2.0Cluster Number of Ca
4 3 2 1 LUCY JULIA FREDARTHUR
JENNIFER
THOMAS
MATTHEW
NICOLE
PAMELA
JOHNOpen the dataset Open the dataset
supermarkets.savsupermarkets.savFrom your N: directory (if you saved it
there last timeOr download it from:
http://www.rdg.ac.uk/~aes02mm/ supermarket.sav •Open it in SPSSThe The supermarkets.savsupermarkets.sav
datasetdatasetRun Principal Run Principal
Components Analysis Components Analysis
and save scoresand save scores •Select the variables to perform the analysis •Set the rule to extract principal components •Give instruction to save the principal components as new variablesCluster analysis: Cluster analysis:
basic stepsbasic steps •Apply Ward's methods on the principal components score •Check the agglomeration schedule •Decide the number of clusters •Apply the k-means methodAnalyse / ClassifyAnalyse / Classify
Select the component Select the component
scoresscoresSelect from hereUntickthis
Select WardSelect Ward''s algorithms algorithm
Click here
firstSelect
method hereOutput: Agglomeration Output: Agglomeration
schedulescheduleNumber of clustersNumber of clusters
Identify the step where the "distance coefficients"makes a bigger jumpThe The screescreediagram diagram
(Excel needed)(Excel needed)Distance
0 100200
300
400
500
600
700
800
Step