Hi, Kassambara. Please make a post/tutorial about K-Prototype Clustering for mixed attribute and how to get the cluster accuracy.

Thankyou for your reply. But the number of observations is 90 lacs or 9 million and not 90.

You can also try the CLARA algorithm (https://www.datanovia.com/en/lessons/clara-in-r-clustering-large-applications/) for large data set.

For me, 90 observations is not a big dataset… But, it depends on the number of variables you have in the dataset

Hi Connie,

My previous comment shows just an example of how to perform clustering on mixed data. Note that, Clara algorithm doesn’t take a distance matrix as input, so you can’t apply it on Gower distance.

For soft clustering, I would suggest the fuzzy clustering method using the fanny() R function [in cluster R package]. It supports distance matrix as an input.

You might be interested by the Hierarchical Clustering on Principal Components (HCPC), which can be also used for performing clustering on mixed data.

Kassambara, thank you for quick reply. Your explanation is always clear and straightforward. As I have 20,000 observations, my first thought is to use CLARA. I will adopt hierarchical clustering as you suggested. As a beginner to cluster analysis, may I ask why hierarchical clustering is better than CLARA in my case? That is the question I need to answer when I write the method section of the paper.

When I read Fraley’s paper (2002), I like the idea of ‘soft’ clustering, which however has some limitations such as large dataset. I don’t want to make things complicated at first place. But in future, after running basic method, is it possible to apply ‘soft’ clustering in my case? Which soft clustering method you recommend? Thank you!

For a mixed data, you can, first, compute a distance matrix between variables using the daisy() R function [in cluster package].

Next, you can apply hierarchical clustering on the computed distance matrix.

For example:

library(cluster)
library(factoextra)

# Load data
data(flower)
head(flower, 3)

# Compute the gower distance matrix and visualize
gower.dist <- daisy(flower, metric = "gower")
fviz_dist(gower.dist)

# Perform aglomerative hierarchical clustering
hc.clust <- agnes(gower.dist)
fviz_dend(hc.clust)