What is Agglomerative Clustering? Agglomerative Clustering Explained
Agglomerative clustering is a hierarchical clustering algorithm used to group similar data points into clusters. It starts by considering each data point as a separate cluster and then progressively merges clusters based on their similarity until a desired number of clusters is obtained. This approach is also known as “bottom-up" or “agglomerative" clustering, as it builds clusters by merging smaller clusters into larger ones.
Here’s how the agglomerative clustering algorithm works:
Initialization: Start by considering each data point as an individual cluster.
Pairwise similarity calculation: Calculate the similarity or dissimilarity between each pair of clusters. The choice of similarity metric depends on the nature of the data and the specific problem. Common distance metrics include Euclidean distance, Manhattan distance, or correlation distance.
Merge clusters: Identify the pair of clusters with the highest similarity and merge them into a single cluster. The merging process continues until the desired number of clusters is reached.
Update similarity matrix: After merging two clusters, the similarity matrix is updated to reflect the new distances between the merged cluster and the remaining clusters. This step involves recalculating the pairwise similarities between the merged cluster and other clusters.
Repeat steps 3 and 4: Continue merging clusters and updating the similarity matrix until the desired number of clusters is obtained.
Agglomerative clustering produces a hierarchy of clusters known as a dendrogram, which represents the clustering process step-by-step. Each level of the dendrogram corresponds to a different number of clusters. The height at which two clusters merge in the dendrogram represents the dissimilarity between them.
The choice of similarity metric, linkage criteria (how to measure the similarity between two clusters), and the stopping criteria for merging clusters (e.g., the maximum number of clusters or a threshold distance) are important parameters in agglomerative clustering.
One advantage of agglomerative clustering is its interpretability and the ability to visualize the clustering hierarchy using dendrograms. However, it can be computationally expensive for large datasets, as pairwise similarity calculations and the maintenance of the similarity matrix requires significant memory and computation resources.
Agglomerative clustering is a widely used technique in various domains, including image segmentation, customer segmentation, document clustering, and biological data analysis.
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