The K-Means clustering algorithm is designed to partition a dataset into K clusters, where each data point belongs to the cluster with the nearest mean. This algorithm aims to minimize the sum of squared errors (SSE) within each cluster, making it highly effective for this purpose.

K-Means 📊: This algorithm minimizes the sum of squared errors within each cluster by iteratively adjusting the centroids and reassigning data points.
DBSCAN 🌐: This density-based clustering algorithm identifies clusters based on the density of points but does not focus on minimizing SSE.
Agglomerative 🔗: This hierarchical clustering method builds a tree of clusters but doesn't directly aim to minimize SSE.
Mean-Shift 📈: This algorithm seeks the mode of the data distribution and forms clusters around these modes, without a focus on SSE minimization.

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