hierarchical-clustering

Why is the line of wss-plot (for optimizing the cluster analysis) looks so fluctuated?

↘锁芯ラ 提交于 2019-12-08 12:35:04
问题 I have a cluster plot by R while I want to optimize the "elbow criterion" of clustering with a wss plot, so I drew a wss plot for my cluster, but is looks really strange and I do not know how many elbows should I cluster, anyone could help me? Here is my data: Friendly<-c(0.533,0.854,0.9585,0.925,0.9125,0.9815,0.9645,0.981,0.9935,0.9585,0.996,0.956,0.9415) Polite<-c(0,0.45,0.977,0.9915,0.929,0.981,0.9895,0.9875,1,0.96,0.996,0.873,0.9125) Praising<-c(0,0,0.437,0.9585,0.9415,0.9605,0.998,0.998

Antipole Clustering

强颜欢笑 提交于 2019-12-08 09:39:47
问题 I made a photo mosaic script (PHP). This script has one picture and changes it to a photo buildup of little pictures. From a distance it looks like the real picture, when you move closer you see it are all little pictures. I take a square of a fixed number of pixels and determine the average color of that square. Then I compare this with my database which contains the average color of a couple thousand of pictures. I determine the color distance with all available images. But to run this

HDBSCAN Python choose number of clusters

十年热恋 提交于 2019-12-08 03:56:23
问题 Is is possible to select the number of clusters in the HDBSCAN algorithm in python? Or the only way is to play around with the input parameters such as alpha, min_cluster_size? Thanks UPDATE: here is the code to use fcluster and hdbscan import hdbscan from scipy.cluster.hierarchy import fcluster clusterer = hdbscan.HDBSCAN() clusterer.fit(X) Z = clusterer.single_linkage_tree_.to_numpy() labels = fcluster(Z, 2, criterion='maxclust') 回答1: If you explicitly need to get a fixed number of clusters

Hierarchical Agglomerative clustering in Spark

﹥>﹥吖頭↗ 提交于 2019-12-07 20:24:23
问题 I am working on a clustering problem and it has to be scalable for a lot of data. I would like to try hierarchical clustering in Spark and compare my results with other methods. I have done some research on the web about using hierarchical clustering with Spark but haven't found any promising information. If anyone has some insight about it, I would be very grateful. Thank you. 回答1: The Bisecting Kmeans Approach Seems to do a decent job, and runs quite fast in terms of performance. Here is a

Pruning dendrogram at levels in Scipy Hierarchical Clustering

家住魔仙堡 提交于 2019-12-07 18:27:59
问题 I have lot of data points which are clustered in the following way using Scipy Hierarchical Clustering. Let's say I want to prune the dendogram at level '1500'? How to do that? (I've tried using 'p' parameter and that is not what I'm expecting) Z = dendrogram(linkage_matrix, truncate_mode='lastp', color_threshold=1, labels=df.session.tolist(), distance_sort='ascending') plt.title("Hierachical Clustering") plt.show() 回答1: As specified in the scipy documentation, if a cluster node is under

sklearn agglomerative clustering with distance linkage criterion

限于喜欢 提交于 2019-12-07 18:06:54
问题 I usually use scipy.cluster.hierarchical linkage and fcluster functions to get cluster labels. However, the sklearn.cluster.AgglomerativeClustering has the ability to also consider structural information using a connectivity matrix , for example using a knn_graph input, which makes it interesting for my current application. However, I usually assign labels in fcluster by either a 'distance' or 'inconsistent' criterion, and AFAIK the AgglomerativeClustering function in sklearn only has the

Color dendrogram branches based on external labels uptowards the root until the label matches

喜欢而已 提交于 2019-12-07 14:42:31
问题 From question Color branches of dendrogram using an existing column, I can color the branches near the leaf of the dendrogram. The code: x<-1:100 dim(x)<-c(10,10) set.seed(1) groups<-c("red","red", "red", "red", "blue", "blue", "blue","blue", "red", "blue") x.clust<-as.dendrogram(hclust(dist(x))) x.clust.dend <- x.clust labels_colors(x.clust.dend) <- groups x.clust.dend <- assign_values_to_leaves_edgePar(x.clust.dend, value = groups, edgePar = "col") # add the colors. x.clust.dend <- assign

How to hierarchically cluster a data matrix in R?

点点圈 提交于 2019-12-06 12:02:52
问题 I am trying to cluster a data matrix produced from scientific data. I know how I want the clustering done, but am not sure how to accomplish this feat in R. Here is what the data looks like: A1 A2 A3 B1 B2 B3 C1 C2 C3 sample1 1 9 10 2 1 29 2 5 44 sample2 8 1 82 2 8 2 8 2 28 sample3 9 9 19 2 8 1 7 2 27 Please consider A1,A2,A3 to be three replicates of a single treatment, and likewise with B and C. Sample1 are different tested variables. So, I want to hierarchically cluster this matrix in

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