Hierarchical Clustering [Biostatistics & Machine Learning]
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Hierarchical Clustering [Biostatistics & Machine Learning]
Hierarchical clustering is a popular machine learning technique used for grouping similar data points into clusters. Unlike other clustering algorithms like K-means, hierarchical clustering doesn't require the number of clusters to be predefined. Instead, it creates a hierarchical decomposition of the dataset, organizing data points into a tree-like structure called a dendrogram.
Machine learning algorithms in unsupervised learning are employed to examine and group unlabeled datasets, uncovering hidden patterns within the data autonomously, thus eliminating the necessity for human intervention, hence the term 'unsupervised'.
How Hierarchical Clustering Works:
1. Distance Calculation:
• The algorithm starts by treating each data point as a separate cluster. It calculates the similarity or dissimilarity between each pair of data points based on a chosen distance metric, such as Euclidean distance.
2. Merging Clusters:
• Next, it merges the two closest clusters into a single cluster. This process continues iteratively until all data points are merged into a single cluster or until a stopping criterion is met.
3. Dendrogram Construction:
• Throughout the merging process, the algorithm constructs a dendrogram, which is a binary tree that represents the hierarchy of clusters. The height of each fusion in the dendrogram corresponds to the distance between the merged clusters.
Advantages of Hierarchical Clustering:
1. No Predefined Number of Clusters:
• Unlike K-means, which requires the number of clusters to be specified beforehand, hierarchical clustering does not need this information, making it more flexible and intuitive.
2. Interpretability:
• The dendrogram visualization allows for easy interpretation of the clustering results, providing insights into the relationships between data points and clusters.
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