cluster-analysis

问题 I am learning about the "kohonen" package in R for the purpose of making Self Organizing Maps (SOM, also called Kohonen Networks - a type of Machine Learning algorithm). I am following this R language tutorial over here: https://www.rpubs.com/loveb/som I tried to create my own data (this time with both "factor" and "numeric" variables) and run the SOM algorithm (this time using the "supersom()" function instead): #load libraries and adjust colors library(kohonen) #fitting SOMs library(ggplot2

问题 I am trying to cluster a grayscale image using Kmeans. First, I have a question: Is Kmeans the best way to cluster a Mat or are there newer more efficient approaches? Second, when I try this: Mat degrees = imread("an image" , IMREAD_GRAYSCALE); const unsigned int singleLineSize = degrees.rows * degrees.cols; Mat data = degrees.reshape(1, singleLineSize); data.convertTo(data, CV_32F); std::vector<int> labels; cv::Mat1f colors; cv::kmeans(data, 3, labels, cv::TermCriteria(cv::TermCriteria::EPS

问题 I am trying to cluster a grayscale image using Kmeans. First, I have a question: Is Kmeans the best way to cluster a Mat or are there newer more efficient approaches? Second, when I try this: Mat degrees = imread("an image" , IMREAD_GRAYSCALE); const unsigned int singleLineSize = degrees.rows * degrees.cols; Mat data = degrees.reshape(1, singleLineSize); data.convertTo(data, CV_32F); std::vector<int> labels; cv::Mat1f colors; cv::kmeans(data, 3, labels, cv::TermCriteria(cv::TermCriteria::EPS

问题 I am trying to run consensus clustering using M3C library in R. My dataset contains 451 samples and ~2500 genes. The row names are the ENTREZ IDs (numeric values) of the genes. I have crosschecked the dataset using "any(duplicated(colnames(MyData)))" command to make sure that there is no duplicate entries in the row names. I ran the following command to perform the consensus clustering using M3C library: res <- M3C(MyData, cores=8, seed = 123, des = annotation, removeplots = TRUE,

问题 I try to create something like this: plotting results of hierarchical clustering ontop of a matrix of data in python Unfortunatelly when I try to execute the code, I get the following warnings: Warning (from warnings module): File "C:\Users\USER1\Desktop\test.py", line 15 Y = sch.linkage(D, method='centroid') ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix Warning (from warnings module): File "C:\Users

问题 I want to calculate the clustering coefficient of each node in the graph using python and Networkx functions. I know there might be a built-in function for this purpose but I want to calculate it by myself but my code is not working. Can someone please point out error? I have tried to test and debug the code. No. of neighbors of each node i.e. n_neighbors are calculated seem to be ok but next code is somehow not running or have some error in it which I'm unable to detect. import matplotlib as

问题 I have a set of data with 50 features (c1, c2, c3 ...), with over 80k rows. Each row contains normalised numerical values (ranging 0-1). It is actually a normalised dummy variable, whereby some rows have only few features, 3-4 (i.e. 0 is assigned if there is no value). Most rows have about 10-20 features. I used KMeans to cluster the data, always resulting in a cluster with a large number of members. Upon analysis, I noticed that rows with fewer than 4 features tends to get clustered together

问题 I'm trying to find a package in R where I can find clusters that exceed a given threshold in a dataset. What I want to know is the the cluster duration/size and the individual values of each cluster. For example (a simple one): I have a vector of data, 10 8 6 14 14 7 14 5 11 12 8 11 11 16 20 6 8 8 6 15 The clusters that are larger than 9 are defined in bold, 10 8 6 14 14 7 14 5 11 12 8 11 11 16 20 6 8 8 6 15 So here the cluster sizes in order are, 1, 2, 1, 2, 4, 1 What I want R to do is

问题 in the cluster analysis the outliers of a dataset can be easily identified by the single-linkage method. Now I would like to remove the outliers automatically. My idea is to remove the data which exceed a specified distance value. Here is my code with the example data of mtcars: library(cluster) library(dendextend) cluster<-agnes(mtcars,stand=FALSE,method="single") dend = as.dendrogram(cluster) In the Plot you can see the resulting dendrogram. The last 4 cars ("Duster 360", "Camaro Z28",

问题 I have a certain problem while implementing multivariate Gaussian distribution for anomaly detection. I have referred the formula from Andrew Ng notes http://www.holehouse.org/mlclass/15_Anomaly_Detection.html below is the problem I face Suppose I have a data set with 2 features and m number of training set i.e n=2 and wants to determine my multivariate Gaussian probability p(x;mu;sigma) which should be a [m*1] matrix because it produces estimated Gaussian value by feature correlation. The