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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SPSS - Using K-means clustering after factor analysis [migrated]

I am a developer that has been tasked with working out how previous results using SPSS were gathered, so we can repeat the process with some new data. We can't ask the person who did the original ...
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Trying to understand xmeans (using R, RWeka)

In a project I want to use XMeans to estimate the 'optimal' number of clusters that are distinguishable in different datasets. The numbers I got seemed too low, so I experimented a bit with generated ...
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2answers
322 views

Do I need to remove duplicates for cluster analysis?

I am doing a cluster analyis and I was wondering whether it is possible to remove duplicates from the data set - in order to increase performance. I work on tables where objects are in rows and ...
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1answer
15 views

what's the implementation of SciKit-Learn K-Means for empty clusters?

SciKit-Learn's K-Means doesn't discard empty clusters (code of particular function here). Instead, it looks for the pattern that is furthest away from its assigned centroid (assigned cluster but I ...
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1answer
41 views

Difference between PCA and spectral clustering for a small sample set of Boolean features

I have a dataset of 50 samples. Each sample is composed of 11 (possibly correlated) Boolean features. I would like to some how visualize these samples on a 2D plot and examine if there are ...
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21 views

Feature selection in clustering

I am looking for a method for feature selection in Gaussian Mixture Models. I have a dataset with 2000 records and 40 variables. I tried to use the "clustvarsel" package in R, which use the BIC as ...
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28 views

Anomaly Analysis (K-Means) - finding suspicious activities/operators

I am relativly new to the field of data mining and want to make a anomaly detection on transactional retail data. I want to use a simple anomaly detection (kmeans at the moment) for finding suspicious ...
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1answer
69 views

K-means clustering exam question [closed]

I have an exam on the k-means algorithm and clustering and I was wondering if anyone knows how to figure out this sample exam question. My teachers are hopeless to provide any information on how to ...
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23 views

k-means random initialization for very-large dataset, is it good enough?

I've got a question in clustering using random k-centers. I ran the k-means algorithm for 10 iteration, for some 100 rows taking 9 random initialization of centroids from the data set itself. The ...
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1answer
25 views

Cluster Analysis: effectiveness of k means results and alternative methods

I have to separate 425 observations based on certain variables numbering 32. 1)I used PCA to reduce the dimensionality of Data, which gave me 32 components out of which 5 components accounted for 75% ...
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1answer
25 views

Interpreting kmeans output

I am working on a clustering model with the kmeans() function in the package stats and I have a question about the output. My data is a sample from several tech companies and AAPL._UP is a variable ...
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37 views

k-means clustering why sum of squared errors (why k-medoids not)?

K-means clustering uses the sum of squared errors (SSE) $E = \sum\limits_{i=1}^k \sum\limits_{p \in C_i} (p-m_i)^2$ (with k clusters, C the set of objects in a cluster, m the center point of a ...
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2answers
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Need a little help understanding K-means++ seeding

I have been working on a project that involves using K-means clustering for generating adaptive palettes from images. I understand the general process of K-means clustering, and I understand the ...
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1answer
72 views

Controling segmentation process in order to get usable segments

My aim is to create segments based on survey data. This in it self is quite straight forward: I use PCA to extract information from the survey answers, and then ...
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13 views

Difference between criterion of k-means?

I am watching a video on k-means clustering here https://www.youtube.com/watch?v=sLf0Z9tCTjE&index=30&list=PL3DFCC23FCE3C7EFB, in which (12:14) the professor briefly mentioned some criterion ...
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95 views

Gaussian Mixture and K-Means ?! a big challenge?

This is taken from Tom. Mitche Material as Old-Exam. I think the (2) is true and not (3). Who can verify me?
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13 views

calculating distance among unordered set partitions for k-mean clustering?

I have a dataset for which I construct unordered set partitions for each data point, e.g. {{1,2,3}{4,6}{5}} for one and {{1,3}{2,4,5}{6}} for the next. I would like to perform k-means clustering on ...
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1answer
28 views

Chosing optimal k and optimal distance-metric for k-means [duplicate]

I have a data-set with roughly 20-dimensions and millions of points which I want to cluster. The goal is to find a set of clusters which: Are as distinct as possible from each other (minimum ...
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K-Means Voronoi Issue: Size?

http://en.wikipedia.org/wiki/Cluster_analysis It states that: K-means separates data into Voronoi-cells, which assumes equal-sized clusters (not adequate here) and ...
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26 views

How to show output for K-means clustering on multi-dimensional data?

I have to implement K-means algorithm for K=10 on handwritten digits data. The data matrix is 2500 X 784,i.e there are 2500 data points each with dimensions 784 .After clustering,I have to label each ...
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1answer
60 views

What is the benefit of using Manhattan distance for K-medoid than using Euclidean distance?

Please give me the reasons. I didn't find any k-medoid example that's calculation is done using Euclidean distance. All examples are made of Manhattan distance for k-medoid.
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1answer
83 views

K-means for non-spherical (non-globular) clusters

It is said that K-means clustering "does not work well with non-globular clusters." However, is this a hard-and-fast rule - or is it that it does not often work? I have a 2-d data set (specifically ...
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7answers
302 views

Comparing k-means results to original data: how to interpret the resulting plots?

I'm running k-means on my dataset that can be found here that has 7 classes. I plotted the ggpairs for the dataset and then took k-means and plotted ggpairs again ...
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42 views

Why do final cluster centers change after applying results from past K-Means clustering (SPSS)?

I have a question regarding what happens after I apply k-means clustering centers to a new data set. Basically, I ran k-means clustering on a dataset1, saved the cluster centers, and applied it to a ...
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1answer
30 views

Statistical Classification Method to Compare PURELY Categorical Data?

I have about a half-dozen variables, each of which can have anywhere from three to ten outcomes. I have to measure the degree of separation/similarity between rows. Either we can do some sort of ...
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1answer
40 views

convert categorical data into numerical data? [closed]

I am doing project for post graduation....project is document clustering.in the project raise the problem is the text data convert into the numerical data?
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1answer
47 views

Probability for selecting centroids - K-means++

K-means++ selects centroids one by one, where each point has the chance to become next centroid with probability proportional to distance to closest centroid already selected. I implemented it like ...
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1answer
37 views

Cluster analysis without knowing the structure of the data set

I’m working on a task regarding cluster analysis for about half a year now, but since the fields of pattern recognition and cluster analysis are quite complex ones, I would call myself a beginner in ...
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54 views

Gaussian mixture models restriction? [closed]

I read this note that with striction on GMM with some condition this algorithm is more like to K-means: the adaptations of the Gaussian mixture models algorithms with Restrict each $\Sigma_i$ ...
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18 views

Clustering not producing even clusters

I'm using k-means clustering to processes running on machines. Dataset sample : machine name, process m1,java m2,tomcat m1,word m3,excel Build a matrix of ...
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55 views

jenks natural breaks vs k-means

I am new to this topic. As far as I know both are data clustering methods. Then my question is when is Jenks prefered over k-means? I read on this website that jenks is particularly suited for ...
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2answers
32 views

k-means nstart equivalent for EM Clustering? Report only the best solution from a large number of initializations?

In K-means clustering, you can specify an nstart=i parameter, which performs the algorithm i times (i.e. selects the initial k random centroids i times) sand reports the best answer only. If I perform ...
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1answer
54 views

K-mean clustering with unknown k

How do I perform k-mean clustering with unknown k? I also need to provide a confidence interval for k. I am thinking in the line of putting a Poisson prior on k. Does that make sense? Does there exist ...
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1answer
40 views

K-Medoids swapping inside clusters

I'm a bit confused with concept of K-medoids. It seems that original algorithm (PAM) describes that swap step should be performed by swaping only one of the medoids with one non-medoid point from ...
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1answer
33 views

K-means++ like initialization for K-medoids

Does it make sense to use initialization in K-medoids like in the case of K-means++? To be precise - is it good to select "farthest" points as initial medoids? (farthest in sense that points that are ...
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1answer
100 views

Understanding cluster plot and component variability

I have run k-means clustering. I have also plotted the results using the following code in R: ...
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490 views

Why does gap statistic for k-means suggest one cluster, even though there are obviously two of them?

I am using K-means to cluster my data and was looking for a way to suggest an "optimal" cluster number. Gap statistics seems to be a common way to find a good cluster number. For some reason it ...
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17 views

Clustering objects with missing values

I have some time-series that I would like to cluster, but they can have missing values. One approach that may be ad hoc is to use an algorithm like K-medoids, and to use similarity measure that will ...
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19 views

I understand the algorithm for k means.But don't understand on how to apply it on testing data

I am having problems not on understanding the k means algorithm but on how to apply it on training ,validation and testing data.Is it like this: Training phase: Apply k-means on the input data and ...
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1answer
25 views

Proof that points change clusters less often as iterations proceed in k means

Is there a way that to prove the following: In k-means clustering, as the iterations proceed, the data points tend to stay in their existing clusters, overall, because the replacement of the centroid ...
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1answer
312 views

Plotting k-means clustering with mixed numerical/categorical data

I have a dataset in CSV format that looks as follows: ...
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2answers
216 views

Clustering a correlation matrix

I have a correlation matrix which states how every item is correlated to the other item. Hence for a N items, I already have a N*N correlation matrix. Using this correlation matrix how do I cluster ...
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1answer
105 views

LDA, PCA and k-means: how are they related?

I am trying to understand how linear discriminant analysis (LDA) is related to principal component analysis (PCA) and k-means clustering method. As an example, here is a comparison between PCA and ...
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1answer
121 views

K-means initial centers membership

I'm trying to plot all the steps of a k-means algorithm with r, but I can't. The k-means algorithm works in this way: Step 1. Initialize the center of the clusters Step 2. Assign the closest ...
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2answers
140 views

Clustering based on large Jensen-Shannon Divergence distance matrix

I have a dataset with large number of features and about 15 000 observations. I’m using a probability distribution distance metric related to Jensen-Shannon divergence (JSD) to cluster the ...
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1answer
271 views

k-means|| a.k.a. Scalable K-Means++

Bahman Bahmani et al. introduced k-means||, which is a faster version of k-means++. This algorithm is taken from page 4 of their paper, Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & ...
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2answers
121 views

K-means++ algorithm

I try to implement k-means++, but I'm not sure how it works. I have the following dataset: (7,1), (3,4), (1,5), (5,8), (1,3), (7,8), (8,2), (5,9), (8,0) From the wikipedia: Step 1: Choose one ...
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1answer
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Determining k in k-means clustering by community detection in graph

I am faced with a problem of choosing an appropriate number of clusters in highly dimensional data. I've read many approaches to determine the number of clusters, and finally came to a solution and I ...
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1answer
160 views

Outlier detection with data (which has categorical and numeric variables) with R

Scenario I have a project about fraud detection where i need to find outliers by kmeans. I have a dataset about bank credits length of 1000. There are 21 columns (14 categorical, 7 numeric ...