Stock market cluster analysis is a technique used to group stocks into similar clusters based on their characteristics and performance. This can be done using various methods, such as hierarchical clustering, k-means clustering, and density-based clustering. The goal of stock market cluster analysis is to identify groups of stocks that have similar characteristics and behave in similar ways, which can help investors make more informed decisions about which stocks to buy and sell. This type of analysis can be useful for various purposes, such as portfolio diversification, risk management, and identifying market trends.
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
