Unsupervised learning is key in handling datasets that lack labels. Unlike supervised methods, it autonomously identifies patterns. This approach includes tasks like clustering and dimension reduction. Clustering segments data into groups with shared attributes. K-means, a popular clustering algorithm, partitions data based on proximity to centroids.

The selection of initial centroids in K-means is crucial. The Elbow Method aids in determining the optimal number of clusters by evaluating Within Cluster Sum of Squares (WCSS). The choice of clusters significantly impacts accuracy, with fewer iterations generally achieving optimal results.

Integrating K-means with financial datasets requires careful preparation, shown in MQL5 environments.
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