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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usefulness of k-means clustering on high dimensional data [duplicate]

I wonder what is the usefulness of k-means clustering in high dimensional spaces, and why it can be better (or not) than other clustering methods when dealing with high dimensional spaces.
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Streaming k-means

I want to perform something like streaming/online/out-of-core kmeans clustering on large data. Here is simple idea: Break all data into N chunks. Read from disk 1st chunk and calculate centroids ...
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Getting the coordinates of every observation at each iteration of kmeans in R [migrated]

I would like to construct an animation of the kmeans clustering algorithm in R. The animation would show each of the observations (rows) in the the dataset plotted in 2 (or 3) dimensions and then ...
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34 views

Alternative to spherical K-Means for clustering large high dimensional dataset

What are some alternatives to Spherical K-Means for clustering very large datasets of high dimension? I'm looking for something that will be fast even on large datasets, and preferably will not ...
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16 views

Clustering Techniques

I'm a little new to data mining and would definitely appreciate some tips. I'm using clustering algorithms looking for possible grouping in some variables described below. I've been using the Excel ...
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33 views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
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27 views

Standardizing variables for k-means?

I only have two variables and they are on the same scale. However, the variance corresponding to the first variable is approximately 0.609, whereas for the second variable is 0.154. So my question is ...
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28 views

Vector Quantization of heavy tailed distribution

I'm generating with Monte Carlo simulation some stock price $X$. Once I have the stock price sample, I want to cluster it with 100 points $\hat{X}$. My problem is that the error associate with my ...
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30 views

Can component scores be used for further analyses, e.g. cluster analysis?

I have done a principal component analysis using SPSS and now have 3 components. 2 components have 4 items in the subscale, and 1 component has 3 items. Component scores using regression for each ...
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28 views

Advice on how to analyse “customer-data” in R

consider the following example data: ...
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21 views

Finding the cluster centers in kernel k-means clustering

I think this is the most easily understood topic in Kernel K Means Clustering. But assuming that I am not an expert in Machine Learning, can someone tell me how does someone calculate Kernel K means ...
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60 views

Using BIC to estimate the number of k in KMEANS

I am currently trying to compute the BIC for my toy data set (ofc iris (: ). I want to reproduce the results as shown here (Fig. 5). That paper is also my source for the BIC formulas. I have 2 ...
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22 views

How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
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27 views

Exact derivation for finding k-means from Gaussian Mixtures

I am having difficulty in deriving k-means from Mixture of Gaussians. I am following the notation from Bishop (2006), Section 9.3.2: Suppose we have : $$ p(\mathbf{x}| \boldsymbol{\mu}_k, ...
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What do you do when a centroid doesn't attract any points?

I am solving a clustering problem on a trivial dataset with the k-means algorithm. I am running a couple of tests and, from time to time, one centroid doesn't attract any points, i.e. I've got an ...
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3answers
66 views

Is it important to scale data before clustering?

I found this tutorial, which suggests that you should run the scale function on features before clustering (I believe that it converts data to z-scores). I'm wondering whether that is necessary. I'm ...
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2answers
68 views

What do you do when there's no elbow point for kmeans clustering

I've learned that when choosing a number of clusters, you should look for an elbow point for different values of K. I've plotted the values of withinss for values of k from 1 to 10, but I'm not seeing ...
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35 views

K-medians, formula to compute the median

If you are running K-medians, and your distance metric is the L1 norm, how do you derive that the center of each centroid is the median of the data points assigned to it? Second, how do you compute ...
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60 views

What algorithm should I use to cluster a huge binary dataset into few categories?

I have a large (650K rows * 62 columns) matrix of binary data (0-1 entries only). The matrix is mostly sparse: about 8% is filled. I would like to cluster it into 5 groups - say named from 1 to 5. I ...
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38 views

Modified K-means with unequal cluster variances

I wonder how I can modify the K-means algorithm so that the cluster volumes are not equal to each other. The K-means objective is to minimize within cluster sum of squares $\sum_{i=1}^{p} {\parallel ...
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33 views

gaussian mixture model - approximate a matrix

I have a similarity matrix M - the value M(i,j) indicates the similarity between two elements i and j. I want to approximate that matrix using a Gaussian Mixture model or I want to cluster that ...
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59 views

Is clustering (kmeans) appropriate for partitioning a one-dimensional array?

I want to group the outcome of a function into 2 (or 3) categories. I have a function efficiency=f(weight,speed,#refueling_stops) that takes 3 input parameters and the output tells me how "efficient" ...
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2answers
119 views

Cluster analysis on panel data

I have a panel data set (country and year) on which I would like to run a cluster analysis by country. My data set has around 20 variables. Here's a summary for my panel data: ...
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41 views

k-medoids algorithm with incomplete distance matrix

I want to apply k-medoids algorithm using an incomplete distance matrix as input. How can I handle the lack of information of this matrix? Just ignoring the missing distances? Or is there a better ...
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84 views

Using K-means for hierarchy clustering, a good approach?

I have a dataset and I would like to see how the dataset is organized via a hierarchy. I have thought of using a divisive method as follows: ...
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Using real data means as centroids for clustering

Suppose we have a data set generated from k different distributions. In k-means, the data classification step (in which we associate each data point to the nearest centroid) uses the current ...
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1answer
50 views

Corrected AIC (AICC) for k-means

I want to calculate the $AIC_c$ (corrected $AIC$) for k-means to decide on the number of clusters, but there is an overfitting problem that I don't know how to solve. Let's say that I have $n$ data ...
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168 views

Cluster Analysis in R

I have a table of employees' information and each employee has the following attributes. I wanted to do an analysis to find out what similarities they share among themselves and possibly cut them into ...
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28 views

Interpreting standardized mean centers in a cluster

I created a $k$-means with 3 clusters. Some of the variables had a big scale, so I used a $z$-score to standardize them. The others (mostly dummies), I left as is. Now, when I create the table of all ...
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22 views

How to compare a data point with the k-means cluster

Say I have the centroids calculated via kMeans, and clusters identified. How do I take an incoming datapoint ( a row in matrix with all the features) to compare with the points and figure out which ...
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1answer
35 views

Longitudinal k-means sample data

Having finished the Coursera's Machine Learning course, I would like to put the theories into practice. Thanks in advance on guiding a newbie! In particular, I am looking forward to some guidance ...
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51 views

When was the k-means clustering algorithm first used?

K-means is probably one of the most used algorithms for clustering. I was looking for bibliography for its first use, but it has been around a lot, so what's the first one? Also, when was the ...
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522 views

Why does k -means clustering algorithm use only euclidean distance metric?

Is there a specific purpose in terms of efficiency, functionality why k-means algorithm do not use cosine similarity as a distance metric and use the euclidean norm
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133 views

Why only the mean value is used in (K-means) clustering method?

In clustering methods such as K-means, the euclidean distance is the metric to use. As a result, we only calculate the mean values within each cluster. And then adjustments are made on the elements ...
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81 views

Does K-means incorporate the K-nearest neighbour algorithm?

I was watching this tutorial on K-means clustering and from what I understand K-means is: Randomly generate the centroids for k clusters Create a classification model dividing into k regions (Do we ...
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What's the methodology behind the most-difference-between-groups-tag-cloud?

What is the likely stats methodology used in this old OKCupid post?: http://www.economist.com/blogs/johnson/2010/10/sexuality_and_language And this: ...
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Data Sets suitable for k-means

My task is to determine which ones of these datasets given in the picture are suitable for k-means. My script says that k-means usually performs well on concave structures, however the sizes of the ...
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33 views

How many different random initializations should I perform with Lloyd's algorithm to obtain the optimal clustering with X% of confidence?

I use Lloyd's algorithm for clustering. Since it relies on a random initialization and Lloyd's algorithm can get stuck in local optima of the k-means objective function, I have to run it several ...
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1answer
86 views

Validity Index Pseudo F for K-Means Clustering

The Validity Index "Pseudo F" is described as: (between-cluster-sum-of-squares / (c-1)) / (within-cluster-sum-of-squares / (n-c)) with c beeing the number of clusters and n beeing the number of ...
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219 views

Clustering inertia formula in scikit learn

I would like to code a kmeans clustering in python using pandas and scikit learn. In order to select the good k, I would like to code the Gap Statistic from Tibshirani and al 2001 (pdf). I would like ...
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2
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109 views

Estimating most important features in a k-means cluster partition

Is there a way to determine which features/variables of the dataset are the most important/dominant within a kmeans cluster solution generated via R?
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150 views

Clustering with K-Means and EM: how are they related?

I have studied algorithms for clustering data (unsupervised learning): EM, and k-means. I keep reading the following : k-means is a variant of EM, with the assumptions that clusters are ...
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2answers
205 views

How to determine which method is the most reasonable clustering results?

Method 1: Cluster by K-means with initial centroid {27, 67.5} Method 2: Cluster by K-means with initial centroid {22.5, 60} Method3: Agglomerative Clustering How can I know which method ...
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Should I standardize my variables for this particular case of cluster analysis?

I'm trying to cluster a list of records based on a (percentage) frequency distribution of variables which add up to 100%. Like Record1 - VarA(25%) VarB(25%) varC(50%) varD(0%) Record2- VarA(50%) ...
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191 views

K-Means clustering after first iteration

In k-means clustering we initially pick $k$ random centroids and assign the given data to one of these $k$ centroids (which ever is nearest). After this we create new centroids by taking the mean of ...
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438 views

Calculate BIC to determine the optimal number of clusters (k-means clustering)

I have a set of data and want to know whether they fall in 1, 2 or 3 groups. I started exploring the question by using k-means in MATLAB. By just looking at the distance from the centroid of each ...
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30 views

Finding area of k-means

My problem is that I have different points (4) in a 3D space each point allows to a class. Other 4 points in a 3D space each point allows to a class. I want to know which of these spaces is more ...
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1answer
123 views

Silhouette coefficients after deleting some data and re-clustering

I was working on a dataset today on which I used K-means clustering algorithm and then calculated the Silhouette coefficients for each point. I then removed 5% of the data with worst silhouette ...
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Standardizing non-normal data for use in distance-based classifier

I have a dataset containing non-normally distributed variables that I want to feed into a distance-based classifier (e.g. K-means). Is it ok to just subtract the mean and divide by the standard ...