Hierarchical Clustering Skill
Hierarchical clustering is an unsupervised machine learning algorithm that organizes data points into a hierarchical tree-like structure, known as a dendrogram. This dendrogram represents the relationships and similarities between data points, enabling the identification of clusters at different levels of granularity.The algorithm starts by treating each data point as a separate cluster and iteratively merges the closest clusters until a single cluster encompasses all data points. The result is a tree structure where the leaves represent individual data points, and internal nodes or branches signify the merging of clusters.Hierarchical clustering can be agglomerative (bottom-up) or divisive (top-down). In agglomerative clustering, data points start as individual clusters and are successively merged, while divisive clustering begins with all data points in one cluster and divides it into smaller clusters.The choice of a merging criterion, such as Euclidean distance or linkage methods like Ward's method, influences the clustering outcome. Hierarchical clustering is versatile and applicable to various domains, including biology, social sciences, and image analysis. Its dendrogram visualization aids in understanding the hierarchical relationships between clusters, offering insights into the natural grouping of data points. However, its computational complexity grows with the number of data points, making it less scalable for very large datasets.
Hierarchical Clustering Sub Skills
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