Cluster Analysis in Research

 Cluster Analysis is a statistical technique used in research to group objects, individuals, or variables into clusters (groups) that are internally homogeneous (similar) and externally heterogeneous (different from other groups). It is widely used in disciplines such as marketing, finance, social science, healthcare, and education for pattern discovery and segmentation.

 Definition

Cluster analysis is an unsupervised learning method that identifies natural groupings within a dataset based on the similarity or distance between data points.

 Purpose of Cluster Analysis

·         To discover patterns in data without prior labeling

·         To segment populations, such as:

o    Investors based on risk preference

o    Customers based on buying behavior

o    Students based on learning styles

·         To reduce data complexity for modeling or visualization

·         To support targeted decision-making

 Types of Cluster Analysis

Method

Description

Example Use Case

Hierarchical Clustering

Builds a tree (dendrogram) of clusters

Classifying mutual funds by risk level

K-Means Clustering

Partitions data into k pre-defined clusters

Grouping investors by portfolio preference

DBSCAN

Density-based clustering for irregular shapes and noise

Detecting fraud clusters in transactions

Two-Step Clustering

Combines hierarchical and k-means; handles large datasets

Survey respondent segmentation

 Steps in Cluster Analysis

1.      Define objectives (e.g., group retail investors by behavior)

2.      Select variables/features (e.g., age, income, risk appetite, investment type)

3.      Standardize data (especially when using mixed units)

4.      Choose clustering method (hierarchical, k-means, etc.)

5.      Determine number of clusters (using elbow method, silhouette score, etc.)

6.      Run clustering algorithm

7.      Interpret and validate clusters

8.      Label and profile clusters for decision-making

 Example in Finance Research

 Research Topic: “Segmenting Retail Investors Based on Investment Behavior”

Variables Used

Type

Age

Numeric

Monthly Investment Amount

Numeric

Preferred Asset Type

Categorical

Risk Appetite

Ordinal

Digital Platform Use

Binary

Result: 3 Clusters Identified

Cluster

Description

1

Young, tech-savvy, high-risk investors

2

Middle-aged, moderate investors

3

Retired, conservative, fixed-income focused

 Tools and Software

·         SPSS (Two-Step Cluster)

·         R (cluster, factoextra packages)

·         Python (scikit-learn, seaborn, matplotlib)

·         Excel (basic distance matrix + dendrograms)

·         Tableau or Power BI (for visualization)

 Advantages

·         No need for predefined categories

·         Reveals hidden structures in data

·         Supports market segmentation, persona creation, and custom targeting

 Limitations

·         Sensitive to scaling and outliers

·         Results depend on method and parameter choice

·         No “correct” number of clusters; requires judgment and validation

 Applications in Different Fields

Field

Example of Cluster Use

Finance

Grouping investors, classifying firms by performance

Marketing

Customer segmentation, product recommendation

Healthcare

Patient categorization based on symptoms or demographics

Education

Grouping students by learning style or engagement level

 

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