# Chapter 9 - Cluster Analysis

Τεχνίτη Νοημοσύνη και Ρομποτική

25 Νοε 2013 (πριν από 4 χρόνια και 7 μήνες)

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Multivariate Data Analysis

Chapter 9
-

Cluster Analysis

Section 3: Independence Techniques

Chapter 9

What Is Cluster Analysis (Q analysis)?

Define groups of homogeneous objects (i.e., individuals,
firms, products, or behaviors)

Maximize the homogeneity of objects within the clusters
while also maximize the heterogeneity between clusters

Segmentation and target marketing

Compare with Factor Analysis

How Does Cluster Analysis Work?

Measuring Similarity (Euclidean distance)

Forming Clusters (hierarchical procedure vs.
agglomerative method)

Determining the Number of Clusters in the Final
Solution (entropy group)

Cluster Analysis Decision Process

Stage One: Objectives of Cluster Analysis

Taxonomy description

Data simplification

Relationship identification

Selection of Clustering Variables

Characterize the objects being clustered

Relate specifically to the objectives of the cluster
analysis

Cluster Analysis Decision Process (Cont.)

Detecting Outliers

Similarity Measures (Interobject similarity)

Correlational Measures

Distance Measures

Comparison to Correlational Measures

Types of Distance Measures (Euclidean distance)

Impact of Unstandardized Data Values (Mahalonobis Distance, D
2
)

Association Measures

Standardizing the Data

Standardizing By Variables (normalized distance
function)

Standardizing By Observation (within
-
case vs. row
-
centering standarlization)

Cluster Analysis Decision Process
(Cont.)

Stage 3: Assumptions in Cluster Analysis

Representativeness of the Sample

Impact of Multicollinearity

Cluster Analysis Decision Process (Cont.)

Stage 4: Deriving Clusters and Assessing Overall Fit

Clustering Algorithms

Hierarchical Cluster Procedures

Ward's Method

Centroid Method

Nonhierarchical Clustering Procedures

Sequential Threshold

Parallel Threshold

Optimization

Selecting Seed Points

Should Hierarchical or Nonhierarchical Methods Be Used?

Pros and Cons of Hierarchical Methods

Emergence of Nonhierarchical Methods

A Combination of Both Methods

How Many Clusters Should Be Formed?

Should the Cluster Analysis Be Respecified

Cluster Analysis Decision Process (Cont.)

Stage 5: Interpretation of the Clusters

Stage 6: Validation and Profiling of the Clusters

Validating the Cluster Solution

Criterion or predictive validity

Profiling the Cluster Solution

Summary of the Decision Process

An Illustrative Example

Stage 1: Objectives of the Cluster Analysis

Segment objects (customers) into groups with
similar perceptions of HATCO

HATCO can then formulate strategies with
different appeals for the separate groups.

Stage 2: Research Design of the Cluster

Analysis

Identify any outliers

Similarity measure (multicollinearity: D
2
)

Stage 3: Assumptions in Cluster Analysis

An Illustrative Example (Cont.)

Stage 4: Deriving Clusters and Assessing

Overall Fit

Step 1: Hierarchical Cluster Analysis

Step 2: Nonhierarchical Cluster Analysis

Stage 5: Interpretation of the Clusters

Two
-
cluster solution

Four
-
cluster solution

Stage 6: Validation and Profiling of the Clusters

Managerial view