To achieve your goal of clustering nodes based on price differences while considering the adjacency matrix, you'll need to adapt your approach to work with the 'ward' linkage method in hierarchical clustering. As you've noted, the 'ward' method requires Euclidean distances, and using inf for disconnected nodes is not compatible with this requirement. Here's a strategy to reframe your problem to use the 'ward' linkage method effectively:
Step 1: Calculate Price Differences
Compute the pairwise price differences between all nodes. For nodes not connected, assign a large but finite value instead of inf to maintain compatibility with Euclidean distances required by the 'ward' method.
Step 2: Hierarchical Clustering
Apply the 'ward' linkage method on your modified distance matrix, which includes the price differences (and large values for disconnected nodes).
Z = linkage(squareform(priceDiffMatrix), 'ward');
Step 4: Determine the Number of Clusters
Decide on the number of clusters, n, based on your requirements. You can use the dendrogram function to visualize the clustering process and help decide on n.
dendrogram(Z)
Step 3: Determine and Form Clusters
Decide on the number of clusters n, and then use the cluster function to assign nodes to clusters.
clusters = cluster(Z, 'maxclust', n);
This approach allows you to cluster nodes based on price differences while considering their connectivity, compatible with the 'ward' method's requirements.
Hope this helps!
