All Questions
9 questions
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
104
views
Why not link features instead of selecting them - Clustering
Currently, I am working on customer segmentation using their purchase data. I plan to use clustering techniques.
So, my data has below info for each customer (9 features and 1 id field)
Now I am ...
- 3,342
0
votes
1
answer
489
views
K-Means clustering - upper bound for number of iterations
Suppose we run the K-means clustering algorithm on a one-dimensional dataset, i.e. $p = 1$, so that each observation consists of a single real number.
We assume that these real numbers are distinct.
...
- 33
3
votes
1
answer
4k
views
KMeans clustering - can inertia increase with number of clusters
I am doing kmeans clusters on sales data and i see that inertia increases for the initial increase in the number of clusters. Can you please explain why that happens?
I am doing Batched Kmeans for the ...
- 85
1
vote
2
answers
12k
views
In k-means clustering, why sum of squared errors (SSE) always decrease per iteration?
In k-means clustering, why sum of squared errors (SSE) always decrease per iteration?
How can prove it by mathematical derivation of formulas?
k : number of clusters
m : number of examples
$c_h$ : ...
- 351
0
votes
0
answers
35
views
Would the result of a K-means clustering run with k = 27 equal the result of three "sub-runs", with total K = 3^3^3?
Suppose I want to try two K-means clustering methods. In the first one, I set k = 27, the algorithm converges, and I get some result set of centroids Y. In the second method, I want to do three "sub-...
- 646
5
votes
2
answers
7k
views
Performing k-means clustering on a set of lines
I have a set of lines (y = numbers between 1 and 100, x= discrete) that I am trying to cluster to group similarly-shaped profiles. I have found that the profiles seem to cluster the cleanest when ...
- 153
0
votes
1
answer
1k
views
Proof that points change clusters less often as iterations proceed in k means
Is there a way that to prove the following:
In k-means clustering, as the iterations proceed, the data points tend to stay in their existing clusters, overall, because the replacement of the centroid ...
- 101
2
votes
1
answer
218
views
Statistics or probabilities associated to each cluster in order to predict if a future datapoint is member of its nearest center
Suppose we have a classical k-means where iteratively each datapoint is assigned to its nearest center.
After a certain time, suppose that we change the dataset by another similar dataset containing ...
- 2,987