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.

learn more… | top users | synonyms

1
vote
0answers
26 views

Normalization/Standarization for Clustering visualization

I'm performing visualization of a dataset clustered with k-means. I compute a weight for each cluster and I draw a circle as big as its weight. But it seems like after the clustering some values are ...
2
votes
1answer
27 views

How to avoid k-means assigning different labels on different run?

I have unlabeled dataset. I am running k-means flat cluster with 2 number of clusters. Every time I run the below program the labels are different. How can I make labels not to change. Is it even ...
1
vote
3answers
38 views

Is K-means performance a bottleneck everywhere?

I've read a paper about a sped-up version of k-means: Ding et al. (2015). Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup. Now I wonder, is k-means' ...
0
votes
0answers
14 views

Which clustering method and number of clusters?

during a cluster analysis procedure, how would I approach finding an appropriate number of clusters within my data? I've been experimenting with kmeans a little doing the following: run kmeans (with ...
1
vote
0answers
31 views

Are negative values acceptable for the Variance Ratio Criterion (VRC)?

I'm using a tutorial for the Variance Ratio Criterion (VRC) by Calinski and Harabasz 1974 (pdf). In my case this entails the following steps: Conduct k-means clustering on my dataset for k=2 to k=...
0
votes
3answers
48 views

Standardizing some features in K-Means

I have 21 features in my dataset, some features are more important than others. As a fact I know, if I don't standardize (mean=0, SD=1) any features, then features with low variance will have slightly ...
0
votes
1answer
47 views

How to find the best clustering method?

I have a data set where the samples are people and the feature are their age, sex, location, job, height, weight… Then I will have a new person with the same information and my goal is the find the ...
-1
votes
1answer
32 views

Explain streaming k-means [closed]

I need to cluster 22 million vectors, each of which has a dimension of 1024. Can anyone explain streaming k-means to me?
0
votes
0answers
11 views

Is centroid came from normalized or reduced data still valid?

I want to incorporate clustering to simplify data and speed up classification execution time. Let's say I have data like this : ...
0
votes
1answer
22 views

DateTime Variable — Kmeans

Suppose I have a DateTime variable that I want to cluster on. I only really care about the day, hour, and minute. Is it wrong to create a column in my data set for each of these or should I keep the ...
1
vote
1answer
24 views

What is the number of free parameters for the k-means algorithm?

When p = the number of data points to cluster k = the number of clusters n = the number of dimensions of each data point what is the number of free parameters ...
1
vote
1answer
35 views

normalisation in k means clustering on percentages and other numerical variables

I have several variables to include in k-means, some of them are percentages (between 0-1) and some of them are numerical variables (positive values). I know normalisation is required when the ...
3
votes
1answer
101 views

Was it as valid to perform k-means on a distance matrix as on data matrix (text mining data)?

(This post is a repost of a question I posted yesterday (now deleted), but I've tried to scale back volume of words and simplify what I'm asking) I'm hoping to get some help interpreting a kmeans ...
0
votes
0answers
33 views

How do I predict original components from PCA analysis?

I have the following dataframe ...
1
vote
1answer
15 views

Within the context of a document term matrix, what exactly are x and y axis in kmeans clustering?

I'm reading up on kmeans and following a blog post to do some text analysis. I watched a helpful video by Andrew Ng fro Coursera which really helped my understanding of what is going on. Here is a ...
1
vote
1answer
65 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. ...
6
votes
2answers
55 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 ...
0
votes
1answer
26 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 ...
0
votes
0answers
13 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 ...
3
votes
0answers
36 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: ...
3
votes
1answer
68 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 ...
0
votes
1answer
47 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 ...
1
vote
1answer
42 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/...
0
votes
0answers
16 views

Clustering for mixed variable type [duplicate]

I have the following data set with mixed variable types ...
-1
votes
2answers
20 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.
0
votes
2answers
28 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 ...
0
votes
0answers
19 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 ...
-1
votes
1answer
31 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 ...
0
votes
1answer
31 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: ...
0
votes
1answer
22 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....
1
vote
4answers
50 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 !
0
votes
0answers
15 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. ...
0
votes
1answer
27 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 ...
0
votes
0answers
27 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: ...
0
votes
0answers
18 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.
0
votes
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 ...
0
votes
0answers
23 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 ...
1
vote
1answer
31 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 ...
0
votes
0answers
20 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. ...
0
votes
0answers
38 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 ...
0
votes
0answers
19 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" ...
0
votes
1answer
22 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 ...
0
votes
0answers
28 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
1
vote
1answer
44 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 ...
0
votes
1answer
15 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 ...
3
votes
1answer
85 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 ...
0
votes
0answers
29 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 ...
0
votes
2answers
30 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). ...
1
vote
0answers
61 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 ...
0
votes
0answers
16 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 ...