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
votes
0answers
23 views

Clustering - Seeking for a solid approach

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 ...
0
votes
0answers
7 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
21 views

Are PAM silhouette and K-means silhouette related ? [on hold]

I used PAM Silhouette with different seeds to find the best k for ...
0
votes
1answer
11 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 ...
-2
votes
0answers
14 views

r: NbClust indices interpretation [closed]

NbClust() is generating about 30 indices which evaluate the "clusterability" of a dataset. How can I interpret the values of these indices? Is it simply a maximization problem, i.e. the higher the ...
0
votes
0answers
14 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
votes
0answers
10 views

How to pass custom Distance function to scikit-learn KMeans clustering [closed]

I m trying to use "Hamming Distance" as a metric for doing K-Means clustering. I couldn't find any example. Please help me with this. I checked the documentation. It doesn't tell any where, how we can ...
0
votes
0answers
8 views

Plot points w/ points colored by cluster? [closed]

I'm trying to develop clusters from a set of 500 points. I've already scaled the data and I used the NbClust package to determine the number of clusters (3). I then ran the k-means algorithm to ...
1
vote
1answer
34 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
12 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
47 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
18 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
9 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
49 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
13 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 ...
-1
votes
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 ...
0
votes
1answer
58 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 ...
0
votes
0answers
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 ...
1
vote
1answer
64 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 ...
0
votes
0answers
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 ...
0
votes
1answer
12 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, ...
1
vote
0answers
42 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 ...
0
votes
2answers
56 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 ...
0
votes
0answers
63 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 ...
0
votes
0answers
28 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 ...
-1
votes
1answer
21 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 ...
1
vote
1answer
61 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 ...
0
votes
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 ...
4
votes
2answers
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 ...
1
vote
0answers
83 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 ...
0
votes
0answers
20 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 ...
1
vote
1answer
35 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: ...
1
vote
1answer
58 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 ...
0
votes
2answers
30 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 ...
0
votes
0answers
14 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 ...
0
votes
0answers
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 ...
0
votes
1answer
25 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: ...
0
votes
1answer
106 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 ...
0
votes
1answer
35 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 ...
0
votes
0answers
16 views

Clustering discrete dataset with strange metric using K-means

I have a dataset of n objects and a matrix A with their correlations. So A[i][j] is a correlation of object i and object j, and I do not know anything more about them. My task is to cluster them by ...
0
votes
1answer
54 views

Proper dataset format for K-Means and DBSCAN clusterers

I'm trying to classify web traffic using clustering algorithms with my own C program, capturing packets with libpcap. In this article K-Means, DBSCAN and AutoClass ...
2
votes
1answer
101 views

detect incorrect term in group of terms

I obtain a 'group' of numbers every day. Each number is associated with a 'term'. eg 35 is Big Data. 42 is Hadoop, 82 is Zebra, 89 is Python, 3 is Machine Learning, and 6 is Waterfall, etc. I want a ...
0
votes
1answer
54 views

Comparing kmeans cluster

I have 150 images, 15 each of 10 different people. So basically I know which image should belong together, if clustered. These images are of 73 dimensions (feature-vector) and I clustered them into ...
1
vote
1answer
36 views

When would I use EM instead of k-means?

When would I want to assign cluster probabilities to patterns instead of hard assignments to clusters? Can someone elaborate?
0
votes
0answers
38 views

k-Means as a preparation phase for supervised learning

I am working on a supervised learning project and I am planning using the $k$-means algorithm to generate clusters, i.e. labels, on a continuous variable in order to apply a Classification SVM. ...
0
votes
1answer
63 views

Compare clustering results based on intra cluster similarity

I am working on a project for my university. A part of this project is to compare the influence of PCA on clustering. Therefore I have a football player dataset that contains a feature called ...
0
votes
0answers
57 views

Different clustering for different rotation of PCA

i´m working on an application which is used for clustering. The process is done in SPSS. As the input-data can have a large number of variables/colummns as a pre-clustering step the PCA is run (via ...
1
vote
3answers
69 views

Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? [duplicate]

Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? In most of the articles online, k-means all deal with l2-norm. L1 norm does not seem to be useful because it is not ...
0
votes
0answers
17 views

K-means on categorical and numeric data [duplicate]

I've seen people use K-means on mixed categorical and numeric data before, however I'm not sure this should be done. Additionally, I've read on this forum that this shouldn't be done. Folks have ...