How Hierarchical Clustering Works?
Hierarchical clustering involves the following steps:
- Calculate the Distance Matrix: Compute the distance between every pair of data points using a distance metric (e.g., Euclidean distance).
- Merge Closest Clusters: Identify the two closest clusters and merge them into a single cluster.
- Update the Distance Matrix: Recalculate the distances between the new cluster and all other clusters.
- Repeat: Repeat steps 2 and 3 until all data points are merged into a single cluster or a stopping criterion is met.
Hierarchical Clustering with Scikit-Learn
Hierarchical clustering is a popular method in data science for grouping similar data points into clusters. Unlike other clustering techniques like K-means, hierarchical clustering does not require the number of clusters to be specified in advance. Instead, it builds a hierarchy of clusters that can be visualized as a dendrogram. In this article, we will explore hierarchical clustering using Scikit-Learn, a powerful Python library for machine learning.
Table of Content
- Introduction to Hierarchical Clustering
- How Hierarchical Clustering Works?
- Dendrograms: Visualizing Hierarchical Clustering
- How to Read a Dendrogram?
- Implementing Hierarchical Clustering with Scikit-Learn
- Advantages and Disadvantages of Hierarchical Clustering
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