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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R Clustering Evaluation (Adaptive Kmeans)

i know there are several threads about this topic, but most i read, most i get confused. I'm doing a project that consists in clustering some data (news articles). I used adaptive Kmeans ...
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K-means clustering in Matlab for feature selection

I am doing feature selection on a cancer data- set which is multidimensional (27803 * 84). I want to try with k-means clustering algorithm in Matlab but how do I decide how many clusters do I want? ...
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How to split a class which is not very cohesive?

Using the silhouette width metric I can find out as to how well each object lies within its class after classification is done. I next find the average silhouette width of objects within a class and ...
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Appropriateness of ANOVA after k-means cluster analysis

The notification after the ANOVA table after K-means analysis indicates that significance levels should not be looked at as the test of equal means, as the cluster solution has been derived based on ...
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Error in K-means cluster analysis - adding initial cluster centers [migrated]

What could be the problem for such an alert in SPSS after I have created initial cluster centers file from hierarchical cluster analysis and wanted to use it in k-means cluster analysis? "The file ...
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27 views

Kmeans cluster size change quite a bit on each run

I am running a kmeans on a sample size of 1000 data. The data is scaled (z). When I run kmeans(df, nstart=25, centers=5)- it runs and I can get the size of each cluster. The largest group has 620 in ...
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Kmeans plotting on discriminant components

When you plot a kmeans model (in R) with the plotcluster() function, it plots the clusters against the axis of the 1st and 2nd discriminant components (dc). In reading about these axis- some state ...
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Clustering using different distance measures [duplicate]

I do unsupervised clustering for a dataset using k-means algorithm. I want to know what is the difference between different distance measures (Euclidean, cityblock, cosine and correlation,...etc). I ...
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2answers
28 views

How to compare two clusterings generated by two clustering approaches

I am currently working on a modification of a clustering algorithm to suit my problem domain. I want to know which methods are available for me to compare the centroids generated from the two ...
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38 views

Outlier detection using k-means in a binary classification problem

I'm using k-means in every class of a binary classification problem and remove samples that have high distance from center of my features (21 features so 21 ...
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91 views

Using k-means with other metrics

So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
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46 views

What's a good way to mentally visualize n dimensions in a k means

I've been using k-means to do some clustering and one of the ideas I'm struggling with is the n dimensions aspect. If I were clustering housing prices vs sq. feet its just a simple 2d graph. That I ...
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63 views

How do I create clusters with a completely categorical data?

I am working on the project that requires data mining. I have been asked to use R. I have a dataset with all categorical variables and would like to form clusters on that. I am unable to figure out ...
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41 views

Correlated variables in kmeans clustering

I have a dataset with 3 variables: A, B and C. Now, A and B are ordinal variables (i.e.; the result of two questions measured using a 5-point Likert), whereas B is continuous. A and B are also ...
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23 views

How to measure the similarity of k-means clustering using different datasets?

I run k-means clustering on my dataset (100 samples in total) and partition the data into k=5 clusters. Then I want to test how robust of the k-means can be; however, I haven't got more new data ...
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23 views

How can I evaluate the accuracy of a clustering when I don't have information on the true class labels?

Already classified data set for the t-shirt factory problem I want to calculate the accuracy of my algorithm. I have the training data without any size information and I couldn't find the classified ...
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67 views

Apache Spark - MLlib - K-Means

I want to perform a K-Means task and fail training the model and get kicked out of Sparks scala shell before I get my result metrics. I am not sure if the input format is the problem or something ...
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10 views

Calculating the Similarity of Survey Responses

I was wondering if anyone had experimented with different functions for calculating the similarity of two sets of survey responses. I am going to be plugging it into a hierarchical clustering algo and ...
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1answer
38 views

Spherical K-means Clustering in R

I have a large data set that I would like to cluster using spherical K means algorithm. However, I am relatively new to this subject and R in general. Most of my knowledge is self taught and I am ...
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60 views

I find very different results using a k-means or two-step clustering method. How is this?

I want to use a cluster analysis (CA) in SPSS to define different profiles in my dataset. I am using different continuous variables for this, including several neuropsychiatric measures. I am new in ...
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19 views

Spectral clustering using Technics other than kmeans

In spectral clustering, the algorithm suggests performing K-means to k eigenvectors of the resulted Laplacian matrix. My question is: 'Can I use other clustering algorithms such as K-medoids or other ...
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103 views

k-means vs k-median?

I know there is k-means clustering algorithm and k-median. One that uses the mean as the center of the cluster and the other uses the median. My question is: when/where to use which?
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143 views

K-means cluster analysis with K=2 as a binary classifier

I used two variables, height and weight, and using K-means cluster analysis with $K=2$, two clusters were obtained. I used $K=2$, as the observations either belong to men or women. I then compared the ...
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58 views

k-means clustering on percentages

Can we do k-means clustering on percentage data (like 56%, 44%, 22%, 13%, etc.)? There is a data set, and data in various parts are measured in percentages.
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34 views

k-means in R generates same number of clusters but different cluster label

Hi there I am running a k-means code in R with the same data and with the same number of clusters, in this case 3, but each time that I run the code, the cluster label changes, for example. In the ...
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1answer
35 views

A clustering and classification question

I'm trying to classify my set of data into two classes (introvert / extrovert). I was thinking of using a decision tree at first, but I don't have any potential known results in order to create my ...
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32 views

Classifying a set of photos to places

I want to cluster photos and map them to places. As input I have Photos with locations (lat, long) Places - some as (imprecise) bounding boxes, some just as points, maybe others as bounding ...
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clustering accuracy

I have a general doubt regarding clustering. I have a data set of size 1196*18675. where 1196 is the no of documents. I am trying to cluster the data with k=7 using k-means. Each time the clustered ...
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42 views

K-means metrics

I have read through the scikit learn documentation and Googled to no avail. I have 2000 data sets, clustered as the picture shows. Some of the clusters, as shown, are wrong, here the red cluster. I ...
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15 views

Turning MiniBatchKMeans into Fuzzy MiniBatchKMeans

I'm using Scikit-Learn, which has an implementation of MiniBatchKMeans. I'm very inexperienced with ML, so I'm wondering how (if ...
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1answer
41 views

Which clustering technique to use for a temporal dataset?

I have seen a similar question but thought I'd ask my own to hopefully garner some usefull feedback. Basically, I have a large temporal dataset, consisting of domestic smart energy meter use ...
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33 views

Avoid local minima in kmeans

Many machine learning techniques suffer from the curse of local minima, one of them is K-means. I am using a matlab script for a computer vision task. One of the first steps I do is kmeans clustering ...
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31 views

K-means Stuck in 1 cluster

I'm working on a problem using the encog Kmeans library and NO MATTER what features I add to the model, it always gets stuck in one of the clusters. ALL of the samples are lumped into one cluster ...
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1answer
78 views

Inputs to k-means are often normalized per-feature. Why not fully whiten the data instead?

We often normalize inputs to the k-means algorithm by 1) subtracting the mean on a per-feature basis and 2) dividing by the standard deviation on a per-feature basis. Some rational behind this is ...
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35 views

Assigning meaningful cluster name automatically

The objective of my work is to cluster the text documents. Once the documents are clustered, traditionally the system will assign numeric value for the clustered group. For example if I have 5 ...
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63 views

K-means cluster Analysis and 4-point Likert Scales

Is there a concern using a 4-point likert-type scale (i.e., agreement) when attempting a cluster analysis using k-means clustering? Most of the data for the items in my data set are favorable (e.g., ...
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98 views

Feature / attribute selection for k-means or other clustering

It seems to me that in literature it is assumed that one knows which features / attributes to choose to characterize an item in clustering. If I have a database with items which have many attributes, ...
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40 views

Edge Probabilty Using KL-Divergence Code in Python

Its a little complicated question, so please bear with me. I am doing Image Segmentation using Swendsen-Wang method for Image Analysis./ (stat.fsu.edu/~abarbu/papers/jcgs.pdf) I have to calculate ...
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93 views

Clustering algorithms for extremely sparse data

I am trying to cluster an extremely sparse text corpus, and I know the number of clusters (my data is the title and author list of scientific publications, for which I already know the number of ...
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62 views

Clustering on a data set with mixed variables

I have a data set consisting of $n$ elements with $d$ features for each element ($x_{i,f}$ means the value for the f-th feature of the i-th element). I would like to cluster this data set into $k$ ...
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Comparing to vectors of labels that contain different labels, but the same number, and the same places

Problem: After doing two cluster analysis, I get two arrays $V_1$ and $V_2$ of length $N$. I have a groundtruth vector as well $GT$. I want to make the comparison, which is label invariant. Say first ...
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ML Bank transactions assignement to invoices

In a effort to reduce human intervention, I'm trying to optimize the process of assigning bank transactions to invoices. This task should be done once every year, so we can assume our dataset won't ...
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379 views

Clusterings that can be caused by K-means

I have gotten the following question as a test question for my exam and I simply cannot understand the answer. A scatter plot of the data projected onto the first two principal components is shown ...
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K-means clustering and R - what to do next?

I used kmeans command on my data-frame (as suggested in "R and Data Mining: Examples and Case studies"). Now my data is clustered into x number of cluster. What ...
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36 views

How to test the significance of clusters?

How can one test the significance of the clusters obtained after a clustering procedure? Are there separate tests for the distance/similarity/dissimilarity measure used to get the distance matrix and ...
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1answer
59 views

Intuition behind the Calinski-Harabasz Index

Given $CH(k) = [B(k) / W(k) ] \times [(n-k)/(k-1)]$, where $n$ = # data points $k$ = # clusters $W(k)$ = within cluster variation $B(k)$ = between cluster variation. It is my understanding that the ...
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81 views

Interpretation of NbClust result

The plots show the output of NbClust(). By looking at the plot, is that correct to say that k=5 is the optimal number of ...
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29 views

Square distance and likelihood in k-means

In k-means algorithm, the distance minimization step is equivalent to maximize likelihood: $P(X|\theta)$ or to maximize posterior distribution $P(\theta|X)$? I think it's more logical to maximize ...
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K-means finds less than K groups [duplicate]

According to the $K$-means algorithm, Randomly assign a group or cluster to each point. # initial step Compute the centroids of all the $K$ groups. Reassign each point to the closest groups by ...
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126 views

Bayesian Networks and discretization of variables using K-means clustering

In many approaches to learning Bayesian Networks a solution to tackle continuous variables is to discretize them and apply one of the well established techniques for learning Bayesian Networks ...