cluster-analysis

I have problem about group in cluster analysis(hierarchical cluster). As example, this is the dendrogram of complete linkage of Iris data set . After I use > table(cutree(hc, 3), iris$Species) This is the output : setosa versicolor virginica 1 50 0 0 2 0 23 49 3 0 27 1 I have read in one statistical website that, object 1 in the data always belongs to group/cluster 1. From the output above, we know that setosa is in group 1 . Then, how I am going to know about the other two species. How do they fall into either group 2 or 3. How did it happen. Perhaps there is a calculation I need to know? I'm

I'm working with k-means in MATLAB. I am trying to create the plot/graph, but my data has three dimensional array. Here is my k-means code: clc clear all close all load cobat.txt; % read the file k=input('Enter a number: '); % determine the number of cluster isRand=0; % 0 -> sequeantial initialization % 1 -> random initialization [maxRow, maxCol]=size(cobat); if maxRow<=k, y=[m, 1:maxRow]; elseif k>7 h=msgbox('cant more than 7'); else % initial value of centroid if isRand, p = randperm(size(cobat,1)); % random initialization for i=1:k c(i,:)=cobat(p(i),:); end else for i=1:k c(i,:)=cobat(i,:);

I'm trying to calculate the Davies-Bouldin Index in Python. Here are the steps the code below tries to reproduce. 5 Steps : For each cluster, compute euclidean distances between each point to the centroid For each cluster, compute the average of these distances For each pair of clusters, compute the euclidean distance between their centroids Then, For each pair of clusters, make the sum of the average distances to their respective centroid (computed at step 2) and divide it by the distance separating them (computed at step 3). Finally, Compute the mean of all these divisions (= all indexes) to

Can anyone help me to calculate F-measure collectively ? I know how to calculate recall and precision, but don't know for a given algorithm how to calculate one F-measure value. As an exemple, suppose my algorithm creates m clusters, but I know there are n clusters for the same data (as created by another benchmark algorithm). I found one pdf but it is not useful since the collective value I got is greater than 1. Reference of pdf is F Measure explained . Specifically I have read some research paper, in which the author compares two algorithms on the basis of F-measure, they got collectively

Does Matlab provide any facility for evaluating clustering methods? (cluster compactness and cluster separation. ....) Or is there any toolbox for it? Not in Matlab, but ELKI (Java) provides a dozen or so cluster quality measures for evaluation. Matlab provides Silhouette index and there is a toolbox CVAP: Cluster Validity Analysis Platform for Matlab. Which includes following validity indexes: Davies-Bouldin Calinski-Harabasz Dunn index R-squared index Hubert-Levin (C-index) Krzanowski-Lai index Hartigan index Root-mean-square standard deviation (RMSSTD) index Semi-partial R-squared (SPR)

I have a dendrogram in R. It is based on hierachical clustering using hclust. I am colouring labels that are different in different colours, but when I try changing the labels of my dedrogram (to the rows of the dataframe the cluster is based on) using dendrogram = dendrogram %>% set("labels", dataframe$column) the labels are replaced, but in the wrong positions. As example: My dendrogram looks like this: ___|___ | _|_ | | | | 1 0 2 when I now try changing the labels like specified above, the labels are changed, but they are applied from left to right in their order in the dataframe. If we