Cluster analysis is the process of grouping data that have similar attributes or properties, and is incredibly useful in a wide variety of fields and applications, including market analysis and segmentation, medical imaging, recommender systems, geospatial data, anomaly detection and more. Whether the number of groups (or “clusters”) are predefined, or determined by an algorithm, cluster analysis helps to provide you with insight about what data should belong together.
This course will provide you with all that you need to get started with cluster analysis. Beginning with a fundamental understanding of what cluster analysis is and how it can be used, you will then go on to learn the most popular clustering algorithms:
- k-means Clustering
- Density-based Spatial Clustering of Applications with Noise (DBSCAN)
- Hierarchical Agglomerative Clustering (HAC)