All Questions
6 questions
2
votes
1
answer
461
views
Meaningful to retrieve original value after standardization using clustering
I already referred these posts here and here.
Currently, I am working on customer segmentation using their purchase data.
So, my data has below info for each customer
Based on the above linked posts ...
- 3,342
2
votes
1
answer
926
views
Interpretation of Cluster Distortion on Normalized data
I have a clustering problem which I solved using KMeans clustering. I also know that the Elbow Method for cluster evaluation can be used to approximate a feasible pick for the number of clusters.
I ...
- 143
0
votes
1
answer
162
views
Normalizing variables before clustering
I am looking to apply k-means clustering on two features of remote sensing data. The first layer is the Normalized Difference Vegetation Index (NDVI), which is expressed on a scale between 0-1. The ...
- 11
1
vote
1
answer
3k
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 ...
- 13
2
votes
1
answer
545
views
Data normalization in k-means and svm
Generally if I want to normalize my data in which direction I should normalize (subtracting mean and dividing by std)?
Lets say I have a data matrix D (...
- 1,119
5
votes
1
answer
3k
views
Inputs to k-means are often normalized per-feature. Why not fully whiten the data instead?
We often normalize inputs to the k-means algorithm by 1) subtracting the mean on a per-feature basis and 2) dividing by the standard deviation on a per-feature basis. Some rational behind this is ...
- 179