The task of clustering involves grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. 🧩

Among the various algorithms used for clustering:

Random Forest: Primarily used for classification and regression tasks, not clustering. 🌲❌
K-Means Clustering: A popular method for partitioning a dataset into K distinct, non-overlapping clusters. Each data point is assigned to the nearest cluster center, and the cluster centers are iteratively adjusted to minimize within-cluster variances. 🎯✅
Support Vector Machines (SVM): Mainly used for classification and regression, with some extensions for clustering (like support vector clustering), but not primarily designed for this purpose. 🔍❌
Hierarchical Clustering: Builds a hierarchy of clusters either in an agglomerative manner (bottom-up) or divisive manner (top-down), useful for discovering nested clusters. 🏰✅

For identifying the optimal clustering of data points, K-Means clustering is one of the most widely used and effective methods. 💡

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