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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How do I predict original components from PCA analysis?

I have the following dataframe ...
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33 views

Clustering in Industry - Why only k-means? [on hold]

How successful is clustering in industry as opposed to academics? More specifically, it seems like clustering algorithms geared towards mixed data type data sets aren't implemented by the common ...
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48 views

Instruct me about K-Means Clustering

I have been instructed by my supervisor to run K-means in Matlab on my data which is comprised of sensory data observations that pertain to 7 outcomes, which I have labeled using numbers from 0 to 7. ...
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2answers
46 views

k-means++ algorithm and outliers

It is well known that k-means algorithm suffers in the presence of outliers. k-means++ is one effective method for cluster center initalization. I was going through the PPT by the founders of the ...
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1answer
23 views

Ideal Inertia for k-mean clustering convergence

Is there an ideal "inertia" for K-mean convergence. For example I'm trying to cluster to 64 clusters using sci-kit. the output is ...
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12 views

Issue on recursive KMeans

I am implementing a recursive KMeans on large population set 1 Million Vectors, each vector has dimension of 1024. Each cluster K_i at level t gives birth to 2 clusters K_2i, K_2i+1 at level t+1 Is it ...
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30 views

Adding weights to functions not accepting weights

If I had a vector of weights for each observation data(iris) wghts <- abs(rnorm(nrow(iris))) And I had a function that did not accept weights as an argument: ...
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1answer
60 views

Why Is Total Sum of Squares Result from K-Means Analysis Different from Variance?

I'm working on a project that requires some clustering analysis. In performing the analysis, I noticed something that seemed odd to me. I understand that in k-means the total sum of squares (total ...
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1answer
34 views

How to cluster an 1-D array by K-means or any other algorithm using scikit-learn? [closed]

I have an one dimensional toy array X. I want to cluster the data into some numbers of clusters.But when I try to fit my data in scikit-learn K-Means function it shows ValueError: n_samples=1 ...
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1answer
40 views

Best Clustering Technique for Probability Scores

I have a data which which have 17 variables i.e. 17-Dimension data. The Data is a result of Max-Diff exercise which is performed for ranking these 17 attributes and have comparative preference/...
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16 views

Clustering for mixed variable type [duplicate]

I have the following data set with mixed variable types ...
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2answers
19 views

Which one has higher square sum error using K-means?

I have trouble in coming out with a straightforward way to know which one is better in K-means when clustering considering SSE(squared sum error). Thanks.
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26 views

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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18 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
29 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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1answer
30 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
19 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 sentence....
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4answers
43 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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13 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
25 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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22 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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17 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
19 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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22 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
27 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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18 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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35 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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17 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
18 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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22 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
42 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
14 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
76 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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25 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
24 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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58 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
129 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
146 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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16 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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44 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
87 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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139 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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43 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
34 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 types,...