Unsupervised learning is gaining traction in AI, offering models the ability to train with only raw data, thus reducing the labor in preparing reference values. Compared to supervised learning, which depends on labeled data, unsupervised learning thrives on detecting patterns independently, useful in scenarios like anomaly detection or data dimensionality reduction. K-means clustering is a prime example, efficient for clustering tasks. It initializes random cluster centers and iteratively assigns data points based on proximity. The algorithm adjusts cluster centers until stability is achieved, and cluster evaluation can be done by analyzing the average deviation of points from their centers.

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#MQL5 #MT5 #Unsupervised