k-means

I am using BOW in opencv for clustering the features of variable size. However one thing is not clear from the documentation of the opencv and also i am unable to find the reason for this question: assume: dictionary size = 100. I use surf to compute the features, and each image has variable size descriptors e.g.: 128 x 34, 128 x 63, etc. Now in BOW each of them are clustered and I get a fixed descriptor size of 128 x 100 for a image. I know 100 is the cluster center created using kmeans clustering. But I am confused in that, if image has 128 x 63 descriptors, than how come it clusters into

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,:);

K-means通常被称为劳埃德算法，这在数据聚类中是最经典的，也是相对容易理解的模型。算法执行的过程分为4个阶段: 1、随机设置K个特征空间内的点作为初始的聚类中心 2、对于其他每个点计算到K个中心的距离，未知的点选择最近的一个聚类中心点作为标记类别 3、接着对着标记的聚类中心之后，重新计算出每个聚类的新中心点（平均值） 4、如果计算得出的新中心点与原中心点一样，那么结束，否则重新进行 第二步过程 sklearn.cluster.KMeans class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm='auto') """ :param n_clusters:要形成的聚类数以及生成的质心数 :param init:初始化方法，默认为'k-means ++',以智能方式选择k-均值聚类的初始聚类中心，以加速收敛;random,从初始质心数据中随机选择k个观察值（行 :param n_init：int，默认值：10使用不同质心种子运行k-means算法的时间

I'm trying to run a KMeans on MLLib from a (large) collection of text documents (TF-IDF vectors). Documents are sent through a Lucene English analyzer, and sparse vectors are created from HashingTF.transform() function. Whatever the degree of parrallelism I'm using (through the coalesce function), KMeans.train always return an OutOfMemory exception below. Any thought on how to tackle this issue ? Exception in thread "main" java.lang.OutOfMemoryError: Java heap space at scala.reflect.ManifestFactory$$anon$12.newArray(Manifest.scala:138) at scala.reflect.ManifestFactory$$anon$12.newArray

I am trying to create a pairs plot of 6 data variables using ggplot2 and colour the points according to the k-means cluster they belong to. I read the documentation of the highly impressive 'GGally' package as well as an informal fix by Adam Laiacano [http://adamlaiacano.tumblr.com/post/13501402316/colored-plotmatrix-in-ggplot2]. Unfortunately, I could not find any way to get the desired output in either. Here is a sample code:- #The Swiss fertility dataset has been used here data_ <- read.csv("/home/tejaskale/Ubuntu\ One/IUCAA/Datasets/swiss.csv", header=TRUE) data_ <- na.omit(data_) u <- c(2

I am trying to run kmeans on a 3 channel color image, but every time I try to run the function it seems to crash with the following error: OpenCV Error: Assertion failed (data.dims <= 2 && type == CV_32F && K > 0) in unknown function, file ..\..\..\OpenCV-2.3.0\modules\core\src\matrix.cpp, line 2271 I've included the code below with some comments to help specify what is being passed in. Any help is greatly appreciated. // Load in an image // Depth: 8, Channels: 3 IplImage* iplImage = cvLoadImage("C:/TestImages/rainbox_box.jpg"); // Create a matrix to the image cv::Mat mImage = cv::Mat(iplImage

I'd like to use silhouette score in my script, to automatically compute number of clusters in k-means clustering from sklearn. import numpy as np import pandas as pd import csv from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score filename = "CSV_BIG.csv" # Read the CSV file with the Pandas lib. path_dir = ".\\" dataframe = pd.read_csv(path_dir + filename, encoding = "utf-8", sep = ';' ) # "ISO-8859-1") df = dataframe.copy(deep=True) #Use silhouette score range_n_clusters = list (range(2,10)) print ("Number of clusters from 2 to 9: \n", range_n_clusters) for n