You're very welcome, thank you for watching my video

Thank you very much for your kind message! Yes, the factoextra package makes it easy to create an elbow plot. Glad to hear you find the video helpful. Kind regards

You're very welcome! Thank you for watching my video

Thank you very much for your kind message. I'm happy to hear that you find my video helpful. Best regards

You're very welcome. Glad to know you find the video helpful. Kind regards

Glad to hear you found the video helpful! Thanks for your kind comment

Thank you very much. Best regards

Thanks for your kind comment on my video! Best regards

Thanks for your kind comment

You're very welcome! Thank you for watching and for commenting on my video

Thanks for your kind comment. The clusters were created using 6 variables. The plots only show 2 variables at a time (2-dimensional plots) so some overlap can be seen. If it were possible to create a 6-dimensional plot then there would be not overlap

It may be because of NAs, kmeans cannot handle data that has NA values. See: stackoverflow.com/questions/36469671/error-in-do-onenmeth-na-nan-inf-in-foreign-function-call-arg-1

You're welcome. You may need to do some data preprocessing to apply k-means to an analysis with likert scale data. You'll have to first apply one-hot encoding so each response/category becomes a binary variable (0 or 1) and then normalize the data to have a mean of 0 and a standard deviation of 1. However note that K-means clustering uses Euclidean distance and assumes that distances between points are meaningful and comparable. This may not be appropriate for likert scale data since likert scale data is ordinal and the distances between responses may not be consistent, so you may consider alternative clustering techniques that are more suited to ordinal data, such as hierarchical clustering or model-based clustering approaches

@@data.ninjas thank you. I think hierarchical will be good

No, it does not. Multicollinearity does not directly influence the cluster analysis results

Hi. That question is not clear. It may mean that from a given dataset select 20 observations (rows) randomly and perform a cluster analysis, or it may mean something else

@@data.ninjas Here is the full question What is 20? Cluster analysis for 20 randomly selected Swiss bank dataset with following requirements 1. Set pseudo random numbers for 20 randomly selected data points 2.write about accuracy, missing values and outliers 3. what is the rationale for selecting a k-means clustering and with a distance function 4. interpret and make comment on clustering output 5. is cluster analysis technique used for dataset is good? Use cluster evaluation 6. visualize 20 selected datapoints by plotting the result of principal components

@@HarpreetKaur-bx1ej The first interpretation was correct. Select 20 rows (data points) from the dataset randomly

@@data.ninjas it means I have to take nstart=20?

Can you please help me in this question as am stuck in it

The kmeans() function in R uses the Hartigan-Wong algorithm by default. Other options are the Lloyd, Forgy and MacQueen algorithms

@@data.ninjas Sir now I need cure algorithm R programming code

Can you please give me your mail id

@@kharankumarr2119 There may not be an implementation of cure algorithm in R yet (or at least I have not found any). There is a Python implementation for cure: github.com/annoviko/pyclustering You may run cure in Python, or you may use the reticulate package in R to work with Python in R rstudio.github.io/reticulate/

@@data.ninjas sir it is a project for us to do it in R programming i am data analytics student of psgcas