问图聚类算法在(著名)虹膜数据集上的应用

EN

 library(igraph)
    library(network)
    library(reshape2)
    library(mstknnclust)
    library(visNetwork)
    library(cluster)
    
    
    /****louvain clustering done on a distance based graph - maybe this is correct****/
    x <- iris[,1:4]
    
    
    dist <- daisy(x,
                       
                        metric = "euclidean"
                       
                        )
    
    d_mat <- as.matrix(dist)
    
     d_long <- melt(d_mat)
    colnames(d_long) <- c("from", "to", "correlation")
    d_mat_long <- d_long[which(d_long$correlation > .5),]
    graph <- graph_from_data_frame(d_mat_long, directed = FALSE)
    
    
     nodes <- as_data_frame(graph, what = "vertices")
    
     colnames(nodes) <- "id"
    nodes$label <- nodes$id
     links <- as_data_frame(graph, what = "edges")
    
    visNetwork(nodes, links) %>%   visIgraphLayout(layout = "layout_with_fr")
     cluster <- cluster_louvain(graph)
    
    nodes$cluster <- cluster$membership
    
    nodes$color <- ifelse(nodes$cluster == 1, "red", "blue")
    
     visNetwork(nodes, links) %>%   visIgraphLayout(layout = "layout_with_fr") %>%   visOptions(selectedBy = "cluster") %>%   visNodes(color = "color")
    
    
    
    
    /***infomap and louvain clustering done a distance based graph but with a different algorithm: I think this is wrong***/
    
    imc <- cluster_infomap(graph)
    membership(imc)
    communities(imc)
    plot(imc, graph)
    
    lc <- cluster_louvain(graph, weights = NULL)
    membership(lc)
    communities(lc)
    plot(lc, graph)
    
    /****mst spanning algorithm on the knn graph : based on the number of clusters I think this is wrong****/
    
    cg <- generate.complete.graph(1:nrow(x),d_mat)
    
    ##Generates kNN graph
    knn <- generate.knn(cg)
    
    plot(knn$knn.graph,
    main=paste("kNN \n k=", knn$k, sep=""))
    
    results <- mst.knn(d_mat)
    
    igraph::V(results$network)$label.cex <- seq(0.6,0.6,length.out=2)
    
    plot(results$network, vertex.size=8,
         vertex.color=igraph::clusters(results$network)$membership,
         layout=igraph::layout.fruchterman.reingold(results$network, niter=10000),
         main=paste("MST-kNN \n Clustering solution \n Number of clusters=",results$cnumber,sep="" ))
    
    /*****louvain clustering and infomap done on the knn graph - maybe this is correct****/
    
    #louvain
    lc <- cluster_louvain(knn$knn.graph, weights = NULL)
    membership(lc)
    communities(lc)
    plot(lc, knn$knn.graph)
    
    imc <- cluster_infomap(knn$knn.graph)
    membership(imc)
    communities(imc)
    plot(imc, knn$knn.graph)