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
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.
- DBSCAN Clustering in ML | Density based clustering
- ML | Hierarchical clustering (Agglomerative and Divisive clustering)
- Difference between CURE Clustering and DBSCAN Clustering
- Different Types of Clustering Algorithm
- K means Clustering - Introduction
- Clustering in R Programming
- Analysis of test data using K-Means Clustering in Python
- Clustering in Machine Learning
- ML | Unsupervised Face Clustering Pipeline
- ML | Determine the optimal value of K in K-Means Clustering
- ML | Mini Batch K-means clustering algorithm
- Image compression using K-means clustering
- ML | Mean-Shift Clustering
- ML | K-Medoids clustering with solved example
- Implementing Agglomerative Clustering using Sklearn
- ML | OPTICS Clustering Implementing using Sklearn
- ML | OPTICS Clustering Explanation
- ML | V-Measure for Evaluating Clustering Performance
- ML | Spectral Clustering
- Python | Clustering, Connectivity and other Graph properties using Networkx
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.