k-means is a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.

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How to find the 1000 closest point to a centroid built from another matrix

I actually work on text-mining. I try to find the 1000 closest documents (inside a corpus of 56000 documents) to a selected corpus of document (150). There are a lot of words in my dictionary. I ...
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23 views

Standard reference for K-means [duplicate]

When citing the K-means algorithm in a paper, what is the standard reference? I ran into this paper through some digging around but I am not entirely sure if this one is still the correct one. I ...
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12 views

Clustering discrete dataset with strange metric using K-means

I have a dataset of n objects and a matrix A with their correlations. So A[i][j] is a correlation of object i and object j, and I do not know anything more about them. My task is to cluster them by ...
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37 views

Proper dataset format for K-Means and DBSCAN clusterers

I'm trying to classify web traffic using clustering algorithms with my own C program, capturing packets with libpcap. In this article K-Means, DBSCAN and AutoClass ...
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72 views

detect incorrect term in group of terms

I obtain a 'group' of numbers every day. Each number is associated with a 'term'. eg 35 is Big Data. 42 is Hadoop, 82 is Zebra, 89 is Python, 3 is Machine Learning, and 6 is Waterfall, etc. I want a ...
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23 views

Comparing kmeans cluster

I have 150 images, 15 each of 10 different people. So basically I know which image should belong together, if clustered. These images are of 73 dimensions (feature-vector) and I clustered them into ...
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27 views

When would I use EM instead of k-means?

When would I want to assign cluster probabilities to patterns instead of hard assignments to clusters? Can someone elaborate?
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30 views

k-Means as a preparation phase for supervised learning

I am working on a supervised learning project and I am planning using the $k$-means algorithm to generate clusters, i.e. labels, on a continuous variable in order to apply a Classification SVM. ...
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30 views

Compare clustering results based on intra cluster similarity

I am working on a project for my university. A part of this project is to compare the influence of PCA on clustering. Therefore I have a football player dataset that contains a feature called ...
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11 views

Matrix factorization: Item vector clustering.

I tried to run k-means clustering (with euclidean distance) on top of item vectors that come from a matrix factorization algorithm. The results make absolutely no sense. Most (95%) items are in the ...
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42 views

Different clustering for different rotation of PCA

i´m working on an application which is used for clustering. The process is done in SPSS. As the input-data can have a large number of variables/colummns as a pre-clustering step the PCA is run (via ...
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49 views

Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? [duplicate]

Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? In most of the articles online, k-means all deal with l2-norm. L1 norm does not seem to be useful because it is not ...
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16 views

K-means on categorical and numeric data [duplicate]

I've seen people use K-means on mixed categorical and numeric data before, however I'm not sure this should be done. Additionally, I've read on this forum that this shouldn't be done. Folks have ...
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25 views

Alternative of canopy clustering algorithm in K-means algorithm

I am analyzing implementation of K-means clustering algorithm in MadLib project. Here K-means algorithm uses Canopy clustering for initial set of Centroid.I am just wondering , are there any other ...
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1answer
49 views

Why is it bad to use Pearson distance in K-means clustering? [duplicate]

I have implemented this algorithm in MATLAB and when I produce plots I notice that using Euclidean distance, I usually get presented with a clear pattern (sum of squares decreases with the number of ...
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15 views

How to plot the results of k-means cluster analysis from Accord?

I'm using the Accord Framework to do K-means clustering with 3 variables. Is there a good way to plot the results of k-means? Are there existing frameworks, who are good in doing so? Is there a way ...
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1answer
73 views

K-Means Clustering using modified correlation (1 - pearson correlation coefficient)

I am trying to implement k-means clustering on a 6x6 data set that looks like this: 2 3 6 0 1 7 4 9 9 6 2 2 0 1 7 9 5 0 2 3 2 7 8 3 8 2 9 2 3 1 8 0 0 1 7 9 Using ...
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22 views

In R package 'cclust' is there an equivalent of 'nstart' option from the 'kmeans' package? [closed]

I am trying to do k-means clustering in R using the cclust package. In k-means clustering, the initial centroid assignment greatly affects the final allocation. The kmeans package has an nstart ...
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K-means - Converting values to percentages - To split or not to split

While attempting to do K-means analysis for the purpose of clustering our player base into different groups of behavior, I came across a few questions. Q1: To simplify the problem, let's say that in ...
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75 views

Proof of convergence of k-means

For an assignment I've been asked to provide a proof that k-means converges in a finite number of steps. This is what I wrote: In the following, $C$ is a collection of all the cluster centres. ...
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87 views

How do H compare multiple runs of K-means?

I have results of best centroids for multiple (10) runs of k-means. How do I compare these weights to check if they are close to each other or different? My goal is to check weather I get to the ...
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2answers
60 views

Using clustering for unsupervised classification (visualizing k-means cluster centers)

I know that the cluster centroid is the middle of a cluster. It's a vector containing one number for each variable, where each number is the mean of a variable for the observations in that cluster. I ...
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201 views

k means for segmenting time series

I understand the fundamentals of k means clustering (though never applied it myself). Now, I am trying to understand how to segment a multivariate time series using k means. I understand that the ...
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50 views

How do i compare two Self Organizing Maps?

I have results (weights) for multiple runs of self organizing map. I am trying to compare these results to check if my algorithm gets to the same solution from different random initial weights. I have ...
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Question regarding K-Means Multilable problem

I have a dataset where for a set of features I have a single label but in my prediction I wanted to predict upto 5 labels for each test data. The labels are categorical and the number of distinct ...
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kmeans convergence text data

I'm using kmeans to cluster text data. My tfidf matrix is approximately 5700 documents x 3900 features, and sparse as is typical with text data. I have set max iterations to converge = 100. I've ...
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Meaning of this Cluster Analysis

I have 801 households (or customers). I have say 100 features on which I will describe a customer. I have a feature map with me. I now apply K Means algorithm for the value of K say 6. I get 6 ...
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21 views

Why is K-Means++ SLOWER than random initialization K-Means?

K-Means is an iterative clustering method which randomly assigns initial centroids and shifts them to minimize the sum of squares. One problem is that, because the centroids are initially random, a ...
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20 views

How to interpret the PAM output

I am using the PAM function in R, and I don't understand how to evaluate its output. Whereas in K-means the ratio between the between sum of squares to the total sum of squares already gives a very ...
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1answer
34 views

Metric for residuals in spherical K-means

I am attempting to use the bag-of-words approach to examine a large text data set. I am experimenting with using spherical K-means to cluster either documents or terms with respect to the other. I ...
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1answer
66 views

K-means in R, high nstart gives tiny clusters (n=1)

I am using kmeans() to cluster standardized scores from a factor analysis in R (20 variables, 919 cases). As R uses random cases for the initial centroids, I was hoping that choosing a high value for ...
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401 views

How to understand the drawbacks of Hierarchical Clustering?

Can someone explain the pros and cons of Hierarchical Clustering? Does Hierarchical Clustering have the same drawbacks as K means? What are the advantages of Hierarchical Clustering over K means? ...
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64 views

What are the differences between Lloyd's, MacQueen's and Hartigan's algorithms for K-Means?

There are three distinct algorithms for the K-Means function in R. These are: Lloyd's MacQueen's Hartigan's I believe I understand how Lloyd's works. 1. The cluster centers are chosen. 2. Points ...
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What is the relation between k-means clustering and PCA?

It is a common practice to apply PCA (principal component analysis) before a clustering algorithm (such as k-means). It is believed that it improves the clustering results in practice (noise ...
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Is K-Medoids really better at dealing with outliers than K-Means? (with example showing the contrary)

K-Medoids and K-Means are two popular methods of partitional clustering. The consensus is that K-Medoids is better at clustering data when there are outliers (source). This is because it chooses data ...
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33 views

Estimating number of clusters using Gap Statistics

Since my application is for streaming data, I chose to use BIRCH to create clusters. BIRCH doesn't produce high quality results, therefore it requires "global clustering step" to improve output ...
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Clustering based on distance measure violating triangle inequality

Suppose I have a set of categorical data $X=\{x_1,x_2,\cdots, x_n\}$, (in my case $n =~ 10,000-50,000$) as well as a precomputed "distance" measure $g(x_i,x_j)$ (in my case I just have an array of ...
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1answer
58 views

why K-means Algorithm will terminate in a finite number of iterations?

I am trying to prove that the K-means algorithm will terminate in a finite number of iterations. But I got stuck on how to get start... and why, intuitively, it will terminate in a finite step? Any ...
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More consistent medoids from Lloyd's algorithm?

I wrote an implementation of Lloyd's algorithm in Python and was running some tests. My data set is 1D (specifically dealing ...
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68 views

K-Means Clustering Not Working As Expcected

I have a script that I'm testing with in Python3 with Scikit to cluster terms based on either words or character n-grams. Basically, it's fed a list of training data with corresponding labels. For ...
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Calculating 'k' for k-Means and Expectation Maximization

This question inspired my question. I've read a lot of articles on the Internet, and it seems like most people use sums of squares to find 'k' for k-Means and they use BIC to find 'k' for Expectation ...
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Calculating Error on a Hold-Out Set

I broke some data into a training and a hold out set. Then I clustered the training set with the k-means method. Now I want to calculate error using the holdout set. Do I just take the square the ...
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Assumption of equal size of clusters in clustering

I am wondering: when clustering data using some general algorithm is there is an assumption on approximately equal sizes of the clusters? For example, in k-means as I know all clusters should have ...
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143 views

Determine the number of clusters for K-means automatically

Since a couple of days I research for a method to determine the number of clusters for K-means automatically, I found elbow method but I can not till now understand its principle. Is there any ...
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k-means with several repetitions

In matlab and python, when running k-means, it is possible to set several repetitions (with random init) so that all of them in the end are combined to have stable global result? I am wondering how ...
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1answer
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Clustering such that each cluster is at least a certain distance away

I have a collection of 2D points in Euclidean space which I want to cluster. However, I want to ensure that in the clusters generated, they are at least a fixed distance away from one another ...
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31 views

K-means clustering matrix - normalized values

I have to perform pairwise correlations and clustering on the rows of a matrix like the following: ...
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R equivalent to SKLearn's MiniBatchKMeans

What is the (is there any) R equivalent of the sklearn's implementation of Mini-Batch KMeans (as they are described in the official documentation) ?
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How can I interpret the results of R kmeans function?

I have a large set of data containing the description for 81432 images. These descriptions are generated by an image descriptor which generates a vector (for each image) with 127 positions. So, I have ...
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Hierarchical K-Means structure and nearest neighbor search

I have just recently begun looking into data structures and their use. The two that stood out the most were k-d tree and Hierarchical K-Means. K-d tree was quite straight forward with search as it's ...