Background
Cluster analysis, also known simply as clustering, involves a variety of techniques intended to arrange objects into groups, or clusters, based on certain characteristics. These methods are designed to identify natural groupings within data sets by maximizing associations within clusters while minimizing associations between different clusters.
Historical Context
The concept of cluster analysis emerged mainly in the mid-20th century as statisticians sought methods to understand and organize complex data sets. Early clustering methods were mainly used in the fields of psychology and biology, but modern cluster analysis has applications across numerous fields, including marketing, finance, and social sciences.
Definitions and Concepts
Cluster analysis: The general name for a number of different methods for grouping objects that have similar characteristics into sets or ‘clusters’. Cluster analysis is used to explore data by sorting different objects into sets so that the degree of association between two objects is maximal if they belong to the same set. It can be used to discover structures in data but provides no explanation for the structure.
Major Analytical Frameworks
Classical Economics
In classical economics, cluster analysis can help identify clusters of goods or populations that respond similarly to economic changes.
Neoclassical Economics
Clustering can be applied to recognize patterns in consumer behaviors, identifying groups that react similarly to changes in prices or income levels.
Keynesian Economics
Cluster analysis might support the assessment of macroeconomic indicators to identify respective clusters that indicate similar economic trends or issues within different regions.
Marxian Economics
It can help differentiate various socio-economic classes or clusters in terms of social relations and economic standing.
Institutional Economics
Cluster analysis can be used to group institutions with similar practices, impact, and regulatory frameworks.
Behavioral Economics
It supports identifying psychological patterns and behaviors among different groups of individuals.
Post-Keynesian Economics
Cluster analysis helps to identify distinct economic phenomena across various cohorts or market segments, often relevant for policy analysis.
Austrian Economics
Clustering can examine different branches of industries, regions or prices, to identify whether spontaneous orderless outcomes occur.
Development Economics
In this field, cluster analysis helps to identify communities or regions with similar developmental challenges or progress levels.
Monetarism
Cluster analysis might be utilized to recognize distinct groups in the effectiveness of monetary policies.
Comparative Analysis
While multiple types of cluster analysis methods exist, understanding which is best for certain economic research is essential. For instance, k-means is efficient for large datasets but requires pre-specification of the number of clusters. Hierarchical clustering doesn’t pre-specify and finds nested clusters, providing more flexibility.
Case Studies
Cluster analysis is often cited in case studies focusing on market segmentation, identifying consumer profiles, regional economic performance, and financial risk groups. Research studies utilizing cluster analysis offer detailed understanding and comparisons across various contexts.
Suggested Books for Further Studies
- “Market Segmentation: How to Do It and How to Profit from It” by Malcolm McDonald.
- “Cluster Analysis” by Brian S. Everitt.
- “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman.
- “Data Mining: Concepts and Techniques” by Jiawei Han, Micheline Kamber, and Jian Pei.
Related Terms with Definitions
- K-Means Clustering: A method of vector quantization used for cluster analysis, particularly in data mining.
- Hierarchical Clustering: A method of cluster analysis which seeks to build a hierarchy of clusters.
- Market Segmentation: The process of dividing a target market into smaller, more defined categories.
- Dimensionality Reduction: The process of reducing the number of random variables under consideration, by obtaining a set of principal variables.
