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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k means clustering for larger text fields

I'm a beginner in data science/machine learning and am attempting to work through some problems on my own I am running a K-means clustering on a dataset consisting of "mission statements". These can ...
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9 views

Dynamic Bag of Words / Features

I'm trying to implement a Bag of Features for a set of images submitted in different moments by a set of users. If the clusters change, then we need to recompute at LEAST all the "visual words" which ...
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1answer
24 views

What are some clustering algorithms in which I can define no of clusters I require?

Is there some other clustering algorithms apart from K-means in which I can define no of clusters I require ?I have a data set of large and skewed data points and K-Means is not providing quite ...
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26 views

Is K-Means used the right way?

I have this model where I have a count of a word. Every day I do a count of the word and then calculate a simple ratio for this word by saying: ...
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1answer
16 views

trouble in understanding outliers' influence on K-means

When outliers are present, the resulting cluster centroids may not be as representative as they otherwise would be and thus, the SSE will be higher as well. However, I don't understand this ...
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3answers
26 views

Why choosing proper initial centroids is very important for K-means?

I don't fully understand why choosing proper initial centroids is very important for K-means. Demos or simple explanations will be very grateful. Thank you !
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8 views

how to cluster the dna sequence from large datasets

i understand how to cluster the numeric data but i m not getting how to cluster the characters.suppose we have large dataset of dna sequence in the form of A,T,C,G.then how to cluster the sequences. ...
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1answer
20 views

How can I use clustering algorithms to bin highly skewed data process?

I have a large set of multi dimensional data.The data points are highly skewed and not smoothly distributed.I want to divide the data set to some finite number of bins.I have approached this problem ...
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21 views

Calculate Probability of Membership from kmeans in R

How can I calculate the probability of membership with R's kmeans output? The output of kmeans is as follows: ...
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16 views

Why that centroids do not change in K-means imply that assignment does not change?

I understand how to prove that assignment does not change lead to that centroids do not change, but I do not feel it is straightforward to prove or disprove it from another direction.
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1answer
17 views

Where do initial document values come from in K-means document clustering?

So the K-means algorithm seems simple enough as I understand it: given some documents, turn those documents into points, initialize some number of k (centroids), assign document-points to nearest ...
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17 views

What is the analog of WSSSE in Gaussian mixture models?

What is the Equivalent of WSSSE (within set sum of square errors) in K-means in GMM (Gaussian mixture models) using the Expectation Maximization algorithm? In detail: if WSSSE is plotted against ...
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1answer
22 views

Convert categorical data with large number of levels to numeric data and what kind of mapping to use

I have a dataset with 20,000 rows and 11 columns. Out of the 11 columns 10 are categorical. Out of the 10, three have very large number of levels. i.e. levels >60. One of the variable is basically ...
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9 views

Using cohort + RFM

I would like to make a cohort analysis, using first order date and some score metric based on RFM. Build some RFM variables. Run a kmeans and classify my customer as: Inactive, shopper, and so on. ...
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31 views

Proof of K-Means convergence in finite iterations

I was asked to prove why having a finite amount of site to cluster assignments eventually leads to convergence. In the Lloyd version of K-means, we minimize the distortion measure at every iteration ...
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39 views

Clustering - Variables transformation and optimal number of clusters

I've been reading quite a bit about best approaches to clustering in cases when the true number of k is unknown. I would love to share my approach (which is a combination of best practices and ...
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11 views

python: how to use cholesky decomposition for whiting the feature matrix before k-means

I would like to use scipy.linalg.cholesky before scipy.cluster.vq.kmeans2 so the clustering will be on the "Mahalanobis" ...
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1answer
14 views

Using centroids to find predictive cluster features

I clustered some data (rows: text documents, columns: word frequencies) using the KMeans implementation in Scikit Learn. This, like most other centroid-based clustering algorithms, returns a centroid ...
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20 views

K means algorithm - computing the centroid using Jaccard distance

How do I compute the centriod of a cluster using the Jaccard distance? Assume I have two sets: A={a,b} and B={b,c}, what is their centroid? JaccardDistance(A,B) = 1- JaccardIndex(A,B) = 1- 1/3 = 2/3 ...
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1answer
38 views

Attribute weighted KMeans

I'm trying to use Kmeans clustering, with an intent to find out clusters by weighting the attributes. Eg. if attribute A matters less than attribute B then the output should put more weight on ...
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1answer
13 views

Using original centroid as cluster identifier after applying PCA

Take a look at my original data. (masked with purely random alphabetic here) : a b c d e f g h i j A = k l m n o p q r s t u v w x y I'm running ...
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1answer
61 views

Why not terminating the k-means clustering algorithm after one iteration?

Does anybody know whether there are applications of the k-means algorithm with only one iteration? (Of course, you may feel inclined to not call it k-means anymore in that case.) There is a clear ...
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22 views

Evaluating kmeans clustering with silhouette coefficient, weird results

I'm performing a kmeans clustering on a 22.000 documents datasets. Not knowing how many clusters I should get, I ran different k values and try to assess the validity of the clusters by determining ...
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2answers
18 views

Determining which cluster to split among several groups using k-mean

I have 3 sets of data that will cluster into 3 distinct groups. Each group is unbalanced meaning that there are different number of points in each cluster (cluster1 = 300, cluster2=50, cluster3=900). ...
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53 views

Cluster validation method for no cluster labels and differently sized clusters

I'm primarily a programmer and have little to no training in formal maths or statistics of any kind. I'm working on my dissertation (which foolishly is about clustering data), the process is ...
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15 views

k.means relation between sum squared error and variance

I work with k-means algorithm and I don't understand the relation between sum squared error and variance. Is there a relation between these values?. I work with k=1. And the values are Sum squared ...
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1answer
23 views

K-means with learning proces

I have a data set in which I already know the cluster to which each individual belong just by empirical observation but I want to predict, given the characteristics of a new individual in which ...
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1answer
84 views

Market Basket Analysis using Clustering to discover *new* product combinations

I have transaction data from a Quick Service Restaurant (QSR) client. Each record in this data set represents a transaction. My objective is to discover products that are the best candidates to be ...
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12 views

Providing an test set for classifnp

I am at moment using predicted.strength to test how well my k-means clustered data classifies using knn. I at moment using the function ...
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1answer
102 views

R - How to fix NbClust error with error message: “The TSS matrix is indefinite. There must be too many missing values.”

I would like to know how I can use clustering methods in R (in this case, Kmeans) if I have an "unkind" input matrix (I get this error log: The TSS matrix is indefinite. There must be too many ...
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15 views

Categorical Clustering of Users Reading Habits

I have a data set with a set of users and a history of documents they have read, all the documents have metadata attributes (think topic, country, author) associated with them. I want to cluster the ...
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1answer
16 views

Algorithm that partitions a set coordinates around X number of fixed centroid coordinates?

I understand that in K-means you select how many clusters you want and the result is the location of each centroid. How does one handle a situation when you know how many and where each centroid is, ...
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43 views

Why is it that a larger 'k' value fails to converge but a smaller 'k' converges?

I'm doing clustering via GMM, which is initialized first by k-means. I am using a data matrix that cannot be classified as small by any standards, they are usually of the size ...
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2answers
67 views

Use a combination of grand mean and group mean centering to standardize variables

I'm using cluster analysis to examine profiles of three variables, X1, X2, and X3. Because ...
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87 views

Cross-validation for k-means clustering in R

I have a dataset of two columns (we can call them x and y). I understand that for cross-validation I need to split my data into k partitions, and for that the general consensus is that I use ...
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31 views

Machine Learning advice for production scheduling

I am new to machine learning, and I have a problem to solve where I think it should be the perfect tool to use. I tried few examples in Azure machine learning platform. Could someone provide me some ...
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1answer
30 views

What kind of data preprocessing is required before running a clustering algorithm?

I have a dataset that consists of 87 observations or rows of data. My variables are a mix of different kinds - continuous, categorical and some count. Examples are variables which are percentage ...
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1answer
67 views

K-means and reproducibility

I'm trying to find the optimal K-means clustering for a set of elements. For a particular K, K-means repeated several times does not always converge to the same clustering due to the randomness in the ...
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1answer
107 views

User segmentation by clustering with sparse data

Imagine that I have 100k users and 1k categories. For each user, up to 5 categories, I know how much money they have spent. Obviously my data is very sparse. Now I want to group users by the money ...
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47 views

Why is the k-means++ algorithm probabilistic?

The k-means++ algorithm provides a technique to choose the initial k seeds for the k-means algorithm. It does this by sampling the next point according to a multinomial distribution over the unchosen ...
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85 views

Outlier similarity

I have a group of sensors where each sensor records 10 different binary data points which is then saved as an integer that can range from 0 to 2^10 - 1. To make sure that's clear, to put it another ...
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22 views

Influential data clustering and regression

I would appreciate it if somebody could give me an easy and quick explanation on the following. My data consists of an outcome variable "Y" and an independent variable "X". The data is linear and ...
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1answer
38 views

How to take the index of the nearest centroid as a feature?

To create additional features for a dataset I have conducted a cluster analysis and assigned a feature to a data set for cluster membership: ...
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1answer
67 views

K-means algorithm's EM “Maximization” step

I'm a software engineer and am trying to understand how Lloyd's K-Means algorithm fits into the general framework of the Expectation-Maximization (EM) algorithm. I previously read the question ...
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2answers
32 views

k-means clustering minimizes conditional variance

I keep reading that K-means clustering "finds cluster centers that minimize conditional variance (good representation of data)". I understand conceptually how K-means clustering works, but please ...
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19 views

K-means algorithm with k=2, what happens with points equidistant from the centroids?

How does the K-means clustering algorithm handle points that are equidistant from all centroids? If you have 2 centroids, where do you assign the point exactly between the two centroids on an ...
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10 views

K-means or Relative Risk - which method is takes more computation

We are trying to solve a decision around modelling perform for destination grouping. We have a large set of destinations (>500k) selected by a set of guests and would like to cluster them together ...
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1answer
27 views

Negative BIC in k-means

Probably a simple question but I'm trying to interpret BIC for k-means. I have some k-means clustering and calculating BIC gives me a negative value, with a plot something like this: ...
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1answer
127 views

A question on cosine similarity & k-means

I used the following code to perform clustering of a dataset in R. distMatrix1 <- dist(sample2, method="cosine") km<-kmeans(distMatrix1,3) I have got some ...
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1answer
38 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 ...