hierarchical-clustering

问题 Let assume that we have X matrix as follows: [[9 0] [1 4] [2 3] [8 5]] Then, from scipy.cluster.hierarchy import linkage Z = linkage(X, method="ward") print(Z) The returning matrix is follows: [[ 1. 2. 1.41421356 2. ] [ 0. 3. 5.09901951 2. ] [ 4. 5. 10. 4. ]] What is the meaning of the returning values? 回答1: Although this has been answered before, it was a "read the docs" answer. I think it is useful to explain the docs a bit. From the docs, we read that: An (n−1) by 4 matrix Z is returned.

问题 Let assume that we have X matrix as follows: [[9 0] [1 4] [2 3] [8 5]] Then, from scipy.cluster.hierarchy import linkage Z = linkage(X, method="ward") print(Z) The returning matrix is follows: [[ 1. 2. 1.41421356 2. ] [ 0. 3. 5.09901951 2. ] [ 4. 5. 10. 4. ]] What is the meaning of the returning values? 回答1: Although this has been answered before, it was a "read the docs" answer. I think it is useful to explain the docs a bit. From the docs, we read that: An (n−1) by 4 matrix Z is returned.

问题 I have a dataset which consists entirely of boolean variables. Exactly like the transformed animal dataset below, only with many more columns. # http://stats.stackexchange.com/questions/27323/cluster-analysis-of-boolean-vectors-in-r library(cluster) head(mona(animals)[[1]]) war fly ver end gro hai ant 0 0 0 0 1 0 bee 0 1 0 0 1 1 cat 1 0 1 0 0 1 cpl 0 0 0 0 0 1 chi 1 0 1 1 1 1 cow 1 0 1 0 1 1 The goal is to rearrange the rows in such a way that groupings of similar membership patterns are

问题 I have a dataset which consists entirely of boolean variables. Exactly like the transformed animal dataset below, only with many more columns. # http://stats.stackexchange.com/questions/27323/cluster-analysis-of-boolean-vectors-in-r library(cluster) head(mona(animals)[[1]]) war fly ver end gro hai ant 0 0 0 0 1 0 bee 0 1 0 0 1 1 cat 1 0 1 0 0 1 cpl 0 0 0 0 0 1 chi 1 0 1 1 1 1 cow 1 0 1 0 1 1 The goal is to rearrange the rows in such a way that groupings of similar membership patterns are

问题 I have a dataset which consists entirely of boolean variables. Exactly like the transformed animal dataset below, only with many more columns. # http://stats.stackexchange.com/questions/27323/cluster-analysis-of-boolean-vectors-in-r library(cluster) head(mona(animals)[[1]]) war fly ver end gro hai ant 0 0 0 0 1 0 bee 0 1 0 0 1 1 cat 1 0 1 0 0 1 cpl 0 0 0 0 0 1 chi 1 0 1 1 1 1 cow 1 0 1 0 1 1 The goal is to rearrange the rows in such a way that groupings of similar membership patterns are

问题 I can not get the color leaves from the scipy dendrogram dictionary. As stated in the documentation and in this github issue, the color_list key in the dendrogram dictionary refers to the links, not the leaves. It would be nice to have another key referring to the leaves, sometimes you need this for coloring other types of graphics, such as this scatter plot in the example below. import numpy as np import matplotlib.pyplot as plt from scipy.cluster.hierarchy import linkage, dendrogram # DATA

问题 I am using the R heatmaply package to produce interactive heatmaps. I like the software, but I would like to get from it the same clustering (ordering of rows and columns) I get using the pheatmap package. Therefore, I would like the two commands to produce the same ouput: heatmaply (scale (mtcars)) pheatmap (scale (mtcars)) Is there a way to do this? Thanks in advance. Arturo P.S. I recently asked another similar question about the color output, i.e., not clustering, output, that was

问题 Consider several points: A = (1, 2.5), B = (5, 10), C = (23, 34), D = (45, 47), E = (4, 17), F = (18, 4) How can I perform hierarchical clustering on them with R? I've read this example Cluster Analysis but I'm not sure how to enter these values as points rather than just regular numbers. When I do x <- c(...) #x values y <- c(...) #y values I can plot them using plot(x,y) But how can I specify those values like in the example: mydata <- scale(mydata) Doing mydata <- scale(x,y) I get the

问题 I would like to know how to assess the quality of the cluster generated by the code below. It is a hierarchical cluster. I know that there are assessment measures for clusters, such as accuracy, recall, F1-measure, Rand Index, among others. Could you help me find the values corresponding to at least two of these metrics? Thank you so much! library(ggplot2) library(rdist) library(geosphere) df<-structure(list(Industries = c(1,2,3,4,5,6), Latitude = c(-23.8, -23.8, -23.9, -23.7, -23.7,-23.7),

来源： https://stackoverflow.com/questions/44834944/agglomerative-clustering-in-sklearn