I have a dataset containing around 800 observations:
It's a dataset collected via a survey; each row is a dataset filled with information re. diet habits, physical activity, the fact of taking supplements or not, personal goals, type of medication (if any) taken and so on.
Majority of features are categorical (aka factors), except one - item_total - which is the amount each user spent on some product.
x My objective is to cluster these questionnaires based on the available features. I use the R language for performing this task.
My current approach is: 1. I calculate gower distance via daisy package aka dissimilarity matrix
gower.dist <- daisy(df[,-1], metric = c("gower"))
where df is the dataframe.
2) I use agglomerative hierarchical clustering.
aggl.clust.c <- hclust(gower.dist, method = "complete")
3) I find the optimal amount of clusters via the silhouette method.
I would like to check other clustering techniques out. Keep in mind that the features are factors so k-mean -ish techniques woulds be underperforming if not impossible at all to apply.
Any suggestions/sources I would need to check out? Thanks in advance! V