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.

Building “Chat with your Data” Apps using Embeddings, ChatGPT, and Cosmos DB for Mongo DB vCore

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Artificial Intelligence (AI), in particular, the advent of OpenAI’s ChatGPT, has revolutionized how we interact with technology. Chatbots powered by this advanced language model can engage users in intricate, natural language conversations, marking a significant shift in AI capabilities. However, one thing that ChatGPT isn’t designed for is integrating personalized or proprietary knowledge – it’s … Read more

How to Use Hierarchical Clustering For Customer Segmentation in Python

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Have you ever found yourself wondering how you can better understand your customer base and target your marketing efforts more effectively? One solution is to use hierarchical clustering, a method of grouping customers into clusters based on their characteristics and behaviors. By dividing your customers into distinct groups, you can tailor your marketing campaigns and … Read more

Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python

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Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more

Cluster Analysis with k-Means in Python

Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more