cluster analysis (clustering)

Cluster analysis or clustering is a machine learning technique that sorts data points into similar groups. It uses the unsupervised machine learning algorithm, which requires no prior information about the data and relies solely on the similarities between the data points.

The basic idea behind this method is to group similar data together and place different data in separate groups. In this way, patterns and structures in the data become apparent. The similarities between the data points are calculated using different metrics.

Applications of cluster analysis

Clustering algorithms are used e.g. in customer segmentation. The algorithm is designed to identify meaningful segments of similar customers. Similarity can be based on master data such as age or gender, transactional data such as number of purchases or basket value, and behavioural data such as number of service requests or length of membership. Based on the identified customer clusters, which lead to a better understanding of the customer, individual campaigns can then be set up (such as a personalised newsletter or other individual offers).

The method is also used for spam filtering, product data analysis and fraud detection.

The many applications of cluster analysis enable organisations to effectively analyse their data and gain valuable insights.

Sources:

https://venturebeat.com/ai/what-is-artificial-intelligence-ai-clustering-how-it-identifies-patterns/

https://www.techtarget.com/searchenterpriseai/definition/clustering-in-machine-learning

https://www.kobold.ai/clustering-guide/ (German)

https://lerneprogrammieren.com/was-bedeutet-clustering-in-der-ki/ (German)