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CLUSTER ANALYSIS Overview
An illustrated tutorial and introduction to cluster analysis using SPSS, SAS, SAS Enterprise Miner, and Stata for examples. Suitable for introductory graduate-level study.
The 2014 edition is a major update to the 2012 edition. Among the new features are these:
Was 89 pages, now book length (207 pages total)
Had 58 figures, now has over 170 illustrations
Now covers Stata as well as SPSS and SAS
Totally revised sections on hierarchical, k-means, and two-step clustering
New coverage of nearest neighbor analysis
New coverage of oblique principal components cluster analysis
New coverage of nonparametric density cluster analysis
New coverage of Kohonen self-organizing map (SOM) clustering
Links to all datasets used in the text.
Below is the unformatted partial table of contents.
Table of Contents
CLUSTER ANALYSIS 1
Overview10
Data examples in this volume10
Key Concepts and Terms12
Terminology12
Distances (proximities)12
Cluster formation12
Cluster validity12
Types of cluster analysis14
Types of cluster analysis by software package14
Disjoint clustering15
Hierarchical clustering15
Overlapping clustering16
Fuzzy clustering16
Hierarchical cluster analysis in SPSS16
SPSS Input for hierarchical clustering16
Example16
The main "Hierarchical Cluster Analysis" dialog17
Statistics button18
Plots button19
Methods button20
SPSS output for hierarchical cluster analysis21
Proximity table21
Cluster membership table22
Agglomeration Schedule22
Dendogram24
Icicle plots27
Summary measures28
Hierarchical cluster analysis in SAS29
SAS input for hierarchical cluster analysis29
Example29
Data setup29
SAS syntax30
SAS output for hierarchical cluster analysis31
Simple statistics table31
Eigenvalues of the covariance matrix table31
Root mean square coefficients32
Cluster history table33
Dendogram34
Icicle Plots36
Cluster membership table36
Saving data to file37
Hierarchical cluster analysis in Stata38
Stata input for hierarchical cluster analysis38
Stata output for hierarchical cluster analysis40
Agglomeration coefficients40
Dendogram41
Saving cluster membership values42
Cluster membership table43
K-means cluster analysis44
Overview44
Example45
K-means cluster analysis in SPSS45
SPSS input45
Main K-means dialog45
The Iterate button47
The Save button48
The Options button49
SPSS Output for K-Means cluster analysis50
The Anova table50
Number of cases in each cluster51
Getting different clusters52
Cluster membership table52
K-Means cluster analysis in SAS53
Overview53
Example54
SAS input for k-means cluster analysis54
SAS output for k-means cluster analysis55
The "Statistics for Variables" table55
Criteria for determining k57
The "Cluster Summary" table60
Cluster membership and distance values61
Crosstabulation tables61
Cluster separation plots62
K-Means cluster analysis in Stata64
Example64
Stata input for k-means cluster analysis64
The main kmeans clustering command64
Obtaining descriptive statistics65
Obtaining distance information65
Obtaining cluster separation plots65
Comparing kmeans and kmedian solutions66
Stata output for k-means cluster analysis66
Cluster membership assignments66
Descriptive statistics67
Distance coefficients69
Cluster separation plots70
Comparing kmeans and kmedians solutions71
Two-step cluster analysis in SPSS72
Overview72
Cluster feature tree (CF tree)73
Proximity73
Example74
SPSS input for two-step clustering74
The main two-step clustering dialog74
Options button dialog75
Output button dialog78
SPSS output for two-step clustering79
Autoclustering table79
Cluster distribution table81
Centroids (cluster profiles) table81
Model summary82
The "Cluster Quality" graph82
The "Cluster Sizes" pie chart82
The "Predictor Importance" chart83
- Sales Rank: #643246 in eBooks
- Published on: 2012-08-29
- Released on: 2012-08-29
- Format: Kindle eBook
Most helpful customer reviews
3 of 3 people found the following review helpful.
Cluster Analysis Crib Sheet
By John M. Ford
This is not a tutorial on the mathematics of cluster analysis nor does it have sufficient examples or depth of explanation to be used as a text. It is a guide to implementation for someone who has learned this material elsewhere, as I did from Everitt, Landau, Leese and Stahl's Cluster Analysis text.
The book begins with an overview of hierarchical, k-means and two-stage cluster analysis techniques along with the associated terms and concepts. Next is a walkthrough of how to set up a cluster analysis in SPSS and interpret the output. A simple example along with screen shots and tables of results is adequate to demonstrate the process. The book then reviews the corresponding procedures in SAS. It closes with a frequently asked questions section that includes some guidance for readers using R. The reference section is brief, but adequately stocked with books that discuss cluster analysis and related techniques in greater detail.
The author accomplishes the book's objectives well enough to make it worth a reader's time. If you are an SPSS or SAS user just learning to conduct a cluster analysis, this book is a useful addition to your reference shelf.
1 of 1 people found the following review helpful.
excellent book
By Amazon Customer
Nice and concise. diagrams and guides are excellent. I own multiple books authored by Dr garson. They serve as the perfect reference for my many projects
1 of 2 people found the following review helpful.
Factor Analysis Superb
By Zoran Radosevic
I enjoyed the book because it provides a clear and concise explanation about the complex statistical analysis. It made me grasp the basic concepts fast, and I enyojed learning from this book. Excellent!
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