Cluster analysis definition
Cluster analysis refers to a popular statistical method used to classify a set of objects into clusters based on their similarities in terms of one or more characteristics. Cluster analysis is often used in data mining, machine learning, and pattern recognition. The output of cluster analysis typically includes a visual representation of the clusters as well as metrics that describe the quality of the clustering and the similarity between data points within and between clusters.
See also: digital information, data segregation
Examples of cluster analysis:
- In marketing, it helps develop targeted campaigns by identifying customer segments based on their demographic, geographic, or behavioral characteristics.
- In biology, it can help organize organisms based on shared characteristics, such as genetic or phenotypic traits.
- In image processing, it helps segment images or signals into different regions based on their similarity.
- In finance, it can identify stocks and bonds exhibiting similar price movements.
How does cluster analysis work:
- Data preparation. Data is prepared by selecting the relevant variables and converting them into a suitable format for analysis.
- Distance calculation. The analysis tool calculates the similarity between each pair of data points.
- Clustering algorithm. The user must choose the appropriate analysis method, such as hierarchical clustering, K-means clustering, or density-based clustering.
- Cluster validation. The clusters are evaluated using calculating metrics such as within-cluster sum of squares (WSS), silhouette score, or purity.
- Interpretation: The results are ready for interpretation and insights.
