Hierarchical Clustering

Hierarchical Clustering

Hierarchical clustering is a popular unsupervised machine learning algorithm used for grouping similar items or observations into clusters. The main idea behind hierarchical clustering is to form a hierarchy of nested clusters by iteratively merging or splitting clusters until a stopping criterion is met.

The two main types of hierarchical clustering are agglomerative and divisive clustering. In agglomerative clustering, each observation starts in its own cluster and the algorithm iteratively merges the two most similar clusters into a new cluster until all observations are in a single cluster. In divisive clustering, all observations start in a single cluster and the algorithm iteratively splits the cluster into two until each observation is in its own cluster.

The similarity or dissimilarity between items is typically measured using a distance metric such as Euclidean distance, Manhattan distance, or cosine similarity. The choice of distance metric depends on the nature of the data and the problem being solved.

The result of hierarchical clustering is usually represented as a dendrogram, which is a tree-like diagram that shows the hierarchy of clusters. Each leaf node of the dendrogram represents an observation, while the internal nodes represent clusters that are formed by merging or splitting other clusters.

The dendrogram can be cut at a certain level to form clusters of desired size or similarity. The cut-off level is usually chosen based on domain knowledge or using a statistical criterion such as the silhouette coefficient or the gap statistic.

Hierarchical clustering has several advantages, including its ability to reveal the underlying structure of the data and its flexibility in handling different types of data. However, it also has some limitations, such as its sensitivity to noise and outliers, and its computational complexity, which can make it difficult to apply to large datasets.

Overall, hierarchical clustering is a useful and widely used technique for exploratory data analysis, pattern recognition, and clustering in a variety of applications such as biology, social sciences, and finance.

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