Fig 1 - Blind Nonlinearity Equalization by Machine Learning based Clustering for Single- and Multi-Channel Coherent Optical OFDM

Conceptual dendrogram for agglomerative and divisive Hierarchical based clustering [19]. Hierarchical clustering is comprised of six steps [20]: 1. Enter the number of targeted clusters, e.g. four for QPSK. 2. Initiate disjoint cluster having zero level (L(0) = 0) and order (m =0). 3. Identify the least unrelated pair of clusters (r, s) w.r.t. D(r,s) = min{d[i,j]} (1) 4. Increase the order by m=m+1 and the clusters r and s into one cluster, creating a new cluster m. The level of such cluster is formed by L(m) = d[r,s] (2)

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Reference

Blind Nonlinearity Equalization by Machine Learning based Clustering for Single- and Multi-Channel Coherent Optical OFDM - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Conceptual-dendrogram-for-agglomerative-and-divisive-Hierarchical-based-clustering-19_fig2_321399805 [accessed 9 Aug, 2022]

Caption

Fig. 1. Conceptual dendrogram for agglomerative and divisive Hierarchical based clustering [19]. Hierarchical clustering is comprised of six steps [20]: 1. Enter the number of targeted clusters, e.g. four for QPSK. 2. Initiate disjoint cluster having zero level (L(0) = 0) and order (m =0). 3. Identify the least unrelated pair of clusters (r, s) w.r.t. D(r,s) = min{d[i,j]} (1) 4. Increase the order by m=m+1 and the clusters r and s into one cluster, creating a new cluster m. The level of such cluster is formed by L(m) = d[r,s] (2)

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<a href="https://www.researchgate.net/figure/Conceptual-dendrogram-for-agglomerative-and-divisive-Hierarchical-based-clustering-19_fig2_321399805"><img src="https://www.researchgate.net/profile/Elias-Giacoumidis/publication/321399805/figure/fig2/AS:631627432603703@1527603123716/Conceptual-dendrogram-for-agglomerative-and-divisive-Hierarchical-based-clustering-19.png" alt="Conceptual dendrogram for agglomerative and divisive Hierarchical based clustering [19]. Hierarchical clustering is comprised of six steps [20]: 1. Enter the number of targeted clusters, e.g. four for QPSK. 2. Initiate disjoint cluster having zero level (L(0) = 0) and order (m =0). 3. Identify the least unrelated pair of clusters (r, s) w.r.t. D(r,s) = min{d[i,j]} (1) 4. Increase the order by m=m+1 and the clusters r and s into one cluster, creating a new cluster m. The level of such cluster is formed by L(m) = d[r,s] (2)"/></a>