Prerequisites: Hierarchical Clustering
The process of Hierarchical Clustering involves either clustering sub-clusters(data points in the first iteration) into larger clusters in a bottom-up manner or dividing a larger cluster into smaller sub-clusters in a top-down manner. During both the types of hierarchical clustering, the distance between two sub-clusters needs to be computed. The different types of linkages describe the different approaches to measure the distance between two sub-clusters of data points. The different types of linkages are:-
1. Single Linkage: For two clusters R and S, the single linkage returns the minimum distance between two points i and j such that i belongs to R and j belongs to S.
2. Complete Linkage: For two clusters R and S, the single linkage returns the maximum distance between two points i and j such that i belongs to R and j belongs to S.
3. Average Linkage: For two clusters R and S, first for the distance between any data-point i in R and any data-point j in S and then the arithmetic mean of these distances are calculated. Average Linkage returns this value of the arithmetic mean.
– Number of data-points in R
– Number of data-points in S
- Different Types of Clustering Algorithm
- ML | Hierarchical clustering (Agglomerative and Divisive clustering)
- DBSCAN Clustering in ML | Density based clustering
- ML | K-Medoids clustering with example
- ML | Mean-Shift Clustering
- ML | Spectral Clustering
- Clustering in Machine Learning
- ML | OPTICS Clustering Explanation
- K means Clustering - Introduction
- Image compression using K-means clustering
- ML | OPTICS Clustering Implementing using Sklearn
- ML | Unsupervised Face Clustering Pipeline
- Implementing Agglomerative Clustering using Sklearn
- ML | V-Measure for Evaluating Clustering Performance
- ML | Determine the optimal value of K in K-Means Clustering
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