Advantages and Disadvantages of Hierarchical Clustering
Advantages:
- No Need to Specify Number of Clusters: Unlike K-means, hierarchical clustering does not require the number of clusters to be specified in advance.
- Dendrogram: Provides a visual representation of the clustering process and helps in determining the optimal number of clusters.
- Versatility: Can be used for various types of data and distance metrics.
Disadvantages:
- Computational Complexity: Hierarchical clustering can be computationally expensive, especially for large datasets.
- Sensitivity to Noise: Can be sensitive to noise and outliers, which may affect the clustering results.
- Lack of Scalability: Not suitable for very large datasets due to its high time complexity.
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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