Efficiently Segment Customers using Hierarchical Clustering in Python
Have you ever found yourself wondering how you can better understand your customer base and target your marketing efforts more … Read more
Clustering
Here, you’ll find all articles related to cluster analysis, whether it’s Centroid-based Clustering (incl. Kmeans), hierarchical clustering, or message passing (Affinity Propagation).
Cluster analysis groups observations into clusters or groups based on their similarity. It is a type of unsupervised learning, which means that the algorithm does not use any prior information about the structure or labels of the data, and discovers the underlying patterns and relationships on its own. The goal of cluster analysis is to identify groups of observations that are similar to each other within the same cluster, and dissimilar to observations in other clusters. This can be useful for various purposes. For example, for identifying natural clusters or groups within a dataset or for data exploration and visualization.
Uncover Hidden Patterns in Financial Markets using Affinity Propagation Clustering in Python
This article introduces affinity propagation – an unsupervised clustering technique that stands out from other clustering approaches by its capacity … Read more
Cluster Analysis with k-Means in Python
Cluster analysis is an unsupervised machine learning technique that groups similar objects into clusters and separates them from different ones. … Read more