Cluster Analysis
- Groups data objects that share similar characteristics to reveal structure in a dataset.
- Commonly used to identify patterns and trends in large or complex datasets.
- Applied in contexts such as customer segmentation and gene expression analysis.
Definition
Section titled “Definition”Cluster analysis is a method of grouping data objects into similar clusters or groups based on the similarity of their characteristics. It is a common technique used in data mining and machine learning to identify patterns and trends in large datasets.
Explanation
Section titled “Explanation”Cluster analysis organizes items in a dataset into clusters so that members of the same cluster are more similar to each other than to members of other clusters. By grouping data objects according to their characteristics, cluster analysis helps reveal patterns and trends that support better understanding and further analysis. It is applicable across a wide range of fields and applications where identifying structure in large or complex datasets is useful.
Examples
Section titled “Examples”Customer segmentation
Section titled “Customer segmentation”A company with a large database of customer information — including demographics, purchasing habits, and other relevant characteristics — can use cluster analysis to group customers into distinct clusters based on similarities such as age, income, and purchasing behavior. This grouping helps the company better understand its customer base and tailor marketing and sales strategies to each cluster.
Gene expression data
Section titled “Gene expression data”Cluster analysis can group genes based on their expression levels across different samples or conditions. Grouping genes with similar expression patterns can help identify genes that behave similarly and potentially uncover biological insights and relationships.
Use cases
Section titled “Use cases”- Data mining and machine learning tasks that require identifying patterns and trends in large datasets.
- Any domain where discovering structure in large or complex datasets is beneficial (as implied by the examples above).
Related terms
Section titled “Related terms”- Data mining
- Machine learning
- Customer segmentation
- Gene expression analysis
- Patterns and trends