Cluster analysis helps identify patterns, relationships, and structures within complex data, enabling informed decision-making, customer segmentation, anomaly detection, and improved understanding of underlying phenomena.
Dear Ammar Kuti Nasser ,
This allows for a deeper understanding of the underlying structure of the data. Likely the largest benefit of using cluster analysis is the ability to find similarities and differences in large datasets can help identify new trends and opportunities for further research.
Cluster analysis involves analyzing a set of data and grouping similar observations into distinct clusters, thereby identifying underlying patterns and relationships in the data.
Cluster analysis is widely used in data analytics across various fields, such as marketing, biology, sociology, and image and pattern recognition.
Cluster analysis varies by the type of clustering algorithm used.
https://careerfoundry.com/en/blog/data-analytics/what-is-cluster-analysis/#:~:text=Identifying%20groups%20and%20relationships&text=This%20allows%20for%20a%20deeper,and%20opportunities%20for%20further%20research.
Regards,
Shafagat
Cluster analysis is a powerful unsupervised learning technique used to group similar data points based on inherent patterns and relationships. It plays a crucial role in various domains by uncovering hidden structures in data, improving interpretability, and aiding in decision-making. One of its key benefits is pattern discovery, allowing businesses to segment customers for targeted marketing, while in finance, it helps assess credit risk and detect fraudulent activities. In healthcare and bioinformatics, clustering assists in disease classification and patient segmentation for precision medicine. Moreover, it is widely used in cybersecurity for anomaly detection, in image processing for object recognition, and in social network analysis to identify communities. Additionally, in fields like the Internet of Vehicles (IoV), it helps optimize traffic flow and detect network anomalies. By reducing dimensionality and filtering noise, cluster analysis enhances predictive modeling and feature selection, making it a fundamental tool for data-driven insights across multiple industries.....
