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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24 views

How is Weka calculating nominal attributes for K-means clustering?

I have both numeric and nominal variables in my dataset. Before applying clustering in Weka, I specified nominal variables to Weka and select K-means clustering. It is good that my nominal data seems ...
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18 views

Selecting the right BIC value

I'm using the hddc for an assignment to find the optimal number of clusters. The dataset is 9-dimensional and consists of 200.000 rows, however, the BIC values that I'm getting are really high. How ...
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1answer
20 views

k-means for set of integers [duplicate]

I'm not really sure where to begin with this but for a simplified example I have a set of integers: {11, 22, 3, 12, 1, 23, 21, 13, 2} I need to partition these into K=3 clusters with initial ...
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50 views

K-means on cosine similarities vs. Euclidean distance (LSA)

I am using latent semantic analysis to represent a corpus of documents in lower dimensional space. I want to cluster these documents into two groups using k-means. Several years ago, I did this using ...
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1answer
79 views

Calculate P value for the correlation coefficient

I would like to understand how people add the P value on a figure for means (Y axis) by age, volume or any other variable (x axis). How did they calculate the P value here? Please check the following ...
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1answer
43 views

k-means cluster, How to re-calculate centroid when using cosine similarity?

I have a requirement using k-means cluster method with cosine similarity instead of Euclidean distance. for example: ...
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1answer
17 views

What is the easiest way to evaluate k-means clustering?

I did clustering with k-means, but I haven't complete my project, now I have to evaluate the result of the k-means clustering, and I want to do that with the easiest way. does anyone have any ...
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1answer
39 views

Best clustering technique for outlier detection?

I have around 15-20 points every second, and I would like to detect outliers based on -their density along x-axis , that means if I am using k-mean clustering then I specify that in x-direction max ...
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11 views

Kalman filter before or after outlier removal?

I am getting radar data points in form of (x,y) coordinate system relative to my position every ms.[around 10-15 data points]. Now, inorder to have better position estimate of the points, I would like ...
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1answer
50 views

Why did K means clustering do a poor job in R

I am trying to implement K means clustering in R, Here is what my data look like: ...
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1answer
27 views

Data normalization in k-means and svm

Generally if I want to normalize my data in which direction I should normalize (subtracting mean and dividing by std)? Lets say I have a data matrix D (...
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6 views

Hand Coordinates Clustering for vector quantization

I've a sequence of pitch, yaw, roll of the hand, plus pitch and yaw of the fingers. So i got a 13-dimensional vector. Which is the best way to understand how to cluster these data in order to perform ...
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66 views

Replicability of Cluster Analysis Solutions / Does Cluster Solution Order Matter

I am performing cluster analysis on a sample (psychology) and I would like to determine how to check the replicability of the cluster solutions. More specifically, I am following the protocol laid out ...
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46 views

K-means as a limit case of EM algorithm for Gaussian mixtures with covariances $\epsilon^2 I$ going to $0$

My goal is to see that K-means algorithm is in fact Expectation-Maximization algorithm for Gaussian mixtures in which all components have covariance $\sigma^2 I$ in the limit as $\lim_{\sigma \to 0}$. ...
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1answer
57 views

Determining number of clusters K-means [duplicate]

I would like to automatically determine the number of clusters for K-means. I have read that elbow method could be used for that. The thing that confuses me is - I have to rerun algorithm while ...
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9 views

Mean of vectors minimizes L2-norm [duplicate]

Recently introduced to data mining and am trying to understand how the mean of vectors minimizes L2-Norm/Euclidean distance. I've tried googling it and can't seem to find a proof as to how something ...
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18 views

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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2answers
35 views

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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14 views

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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3answers
117 views

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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2answers
30 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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20 views

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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2answers
29 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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1answer
51 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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2answers
106 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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2answers
49 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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70 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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1answer
46 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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27 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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25 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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88 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
57 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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82 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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1answer
20 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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199 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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3answers
150 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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1answer
73 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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1answer
40 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
38 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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1answer
33 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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2answers
60 views

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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1answer
47 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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19 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
43 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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41 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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33 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 ...
3
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1answer
93 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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1answer
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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1answer
79 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., ...