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.
[Tex]L(R,S) = \frac{1}{n_{R}\times n_{S}}\sum_{i=1}^{n_{R}}\sum_{j=1}^{n_{S}} D(i,j), i\in R, j\in S[/Tex]
where,
- [Tex]n_{R}[/Tex] : Number of data-points in R
- [Tex]n_{S}[/Tex] : Number of data-points in S
ML | Types of Linkages in Clustering
Prerequisites: Hierarchical Clustering
Hierarchical clustering is a versatile technique used in machine learning and data analysis for grouping similar data points into clusters. This process involves organizing the data points into a hierarchical structure, where clusters are either merged into larger clusters in a bottom-up approach (agglomerative) or divided into smaller clusters in a top-down approach (divisive). Regardless of the direction, the computation of distances between sub-clusters is crucial in hierarchical clustering.
The various types of linkages describe distinct methods for measuring the distance between two sub-clusters of data points, influencing the overall clustering outcome.
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