All Questions
33 questions
445
votes
5
answers
177k
views
How to understand the drawbacks of K-means
K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just ...
- 6,919
64
votes
3
answers
68k
views
Clustering with K-Means and EM: how are they related?
I have studied algorithms for clustering data (unsupervised learning): EM, and k-means.
I keep reading the following :
k-means is a variant of EM, with the assumptions that clusters are
...
- 793
35
votes
1
answer
35k
views
Difference between standard and spherical k-means algorithms
I would like to understand, what is the major implementation difference between standard and spherical k-means clustering algorithms.
In each step, k-means computes distances between element vectors ...
- 1,329
31
votes
8
answers
36k
views
Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
I want to perform K-means clustering on objects I have, but the objects aren't described as points in space, i.e. by objects x features dataset. However, I am able ...
- 313
26
votes
2
answers
26k
views
If k-means clustering is a form of Gaussian mixture modeling, can it be used when the data are not normal?
I'm reading Bishop on EM algorithm for GMM and the relationship between GMM and k-means.
In this book it says that k-means is a hard assign version of GMM. I'm wondering does that imply that if the ...
- 527
22
votes
3
answers
38k
views
Do I need to drop variables that are correlated/collinear before running kmeans?
I am running kmeans to identify clusters of customers. I have approximately 100 variables to identify clusters. Each of these variables represent the % of spend by a customer on a category. So, if I ...
- 221
18
votes
4
answers
13k
views
Are there any non-distance based clustering algorithms?
It seems that for K-means and other related algorithms, clustering is based off calculating distance between points. Is there one that works without it?
- 578
14
votes
3
answers
42k
views
Why do we use k-means instead of other algorithms?
I researched about k-means and these are what I got: k-means is one of the simplest algorithm which uses unsupervised learning method to solve known clustering issues. It works really well with large ...
11
votes
1
answer
26k
views
K-means: How many iterations in practical situations?
I don't have industry experience in data mining or big data so would love to hear you sharing some experience.
Do people actually run k-means, PAM, CLARA, etc. on a really big dataset? Or they just ...
- 155
11
votes
3
answers
12k
views
Is it true that K-Means has an assumption "each cluster has a roughly equal number of observations"?
A lecturer claimed in a recent class that "K-means assumes that each cluster includes a roughly equal number of observations."
However, when I searched online, there is conflicting information ...
- 273
6
votes
1
answer
3k
views
When should I use k-means instead of Spectral Clustering?
From the image linked to below, it looks like when the data actually consists of K isotropic clusters, Spectral Clustering does as well as K-means. But for other, non-convex clusters, Spectral ...
- 271
3
votes
1
answer
21k
views
Stopping condition of K-means
I know that K-means algorithm stops when the cluster assignment does not change or just changes a little. Apart from that, and defining the maximum number of iterations, is there any other stopping ...
- 155
3
votes
2
answers
3k
views
Extremely near centroids in k-means?
In the k-means algorithm, what happens if two of the initially chosen centroids are extremely near to each other?
Say I have two centroids c1 and c2, and d(c1, c2) ~ 0, i.e. the distance between c1 ...
- 141
2
votes
2
answers
3k
views
Automating determination of number of clusters from a kmeans cluster analysis
I use kmeans for clustering a set of data. However, I have to specify the number of clusters. The problem is that sometimes I need 2 and other times I need 3 clusters.
Is there a clustering algorithm ...
- 353
2
votes
2
answers
881
views
Jenks Natural breaks - Interpreting Goodness of Variance Fit
I am trying to find breaks in a multiple continuous type variables.
So, I tried the jenks natural breaks algorithm.
Based on the code from here, I managed to find ...
- 3,342
2
votes
1
answer
3k
views
K-means calculate MSE in Weka
I am doing some clustering analysis with Weka and decided to apply the k-means algorithm (the clusterer SimpleKMeans).
On my first analysis I ran the algorithm with 2 clusters.
Then, after finding ...
- 237
2
votes
1
answer
4k
views
k-means with binary variables
I have converted all of my features to binary variables. now I have 21 features in my data set. I am trying to cluster them with k-means. I used Hamming distance in order to measure the distance ...
- 295
2
votes
1
answer
5k
views
silhouette score vs Distortion score
I am working on segmenting my customers with clustering. My dataset size is 7315 rows and 30 features.
So, as a beginner to clustering, I passed all my 29 features (excluding id column) to the cluster....
- 3,342
2
votes
1
answer
404
views
Clustering related areas with k-means in WEKA
I am trying to cluster related areas of knowledge. A sample of my file is:
...
- 115
1
vote
3
answers
7k
views
Clustering with 3 attributes
Please bear with me because I am very new to data mining.
I have a database of 3 attributes: latitude, longitude and temperature. I want to find clusters for the temperature data and I also want to ...
- 129
1
vote
1
answer
96
views
Find all possible clusterizations
I need help to find all possible clusterizations via the k-means method in Python. Let's assume for simplicity that I have the following table:
height | weight | country of origin (X/Y/Z) | flag (1/0)
...
- 131
1
vote
1
answer
1k
views
What hypothesis should I have for k-means clustering?
I am wondering if we can just run k-means clustering without any hypothesis to find the optimal clusters or finding the optimal clusters are the hypothesis for K-means clustering? What hypothesis ...
- 1,733
1
vote
1
answer
250
views
Understanding the quality of the KMeans algorithm
After reading Unbalanced factor of KMeans, I am trying to understand how this works. I mean, from my examples, I can see that the less the value of the factor, the better the quality of KMeans, i.e. ...
- 133
1
vote
1
answer
995
views
How to perform Normalization on Call Details Record to perform k-Mean Clustering
I'm new to data mining and currently doing mining project on telecom customer segmentation (based on profile and call details record). I have gender, age, call time and call duration and have to ...
- 135
1
vote
1
answer
79
views
Distance function that captures both circular and "appear as line" clusters [closed]
based on what I know in k-mean clustering, if i use single linkage distance it can capture clusters of thread shapes but it is not suitable for capturing circular clusters.
Also If we use complete ...
- 11
1
vote
0
answers
225
views
Comparing clustering stability over period of time [duplicate]
Suppose I am applying K-means clustering on two datasets generated by same process. How to compare clustering stability over a period of time? Let's say I have applied clustering in 2016 on a data set,...
- 333
1
vote
0
answers
126
views
Clustering Techniques
I'm a little new to data mining and would definitely appreciate some tips.
I'm using clustering algorithms looking for possible grouping in some variables described below.
I've been using the Excel ...
- 11
1
vote
1
answer
266
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 ...
- 11
0
votes
2
answers
678
views
How to compare the distributions of variables within clusters?
I used K-means to cluster 15k data points composed of 5 quantitative features scaled between 0 and 1.
I would like to compare the distributions of the features within each cluster, and also compare ...
- 293
0
votes
1
answer
311
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
-1
votes
1
answer
439
views
k-medoids algorithm with incomplete distance matrix
I want to apply k-medoids algorithm using an incomplete distance matrix as input. How can I handle the lack of information of this matrix? Just ignoring the missing distances? Or is there a better way?...
- 909
-1
votes
2
answers
3k
views
Meaning of this Cluster Analysis
I have 801 households (or customers). I have say 100 features on which I will describe a customer.
I have a feature map with me. I now apply K Means algorithm for the value of K say 6. I get 6 ...
- 309
-1
votes
2
answers
135
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 ...
- 101