Clustering for Categorical and Numerical data

Question

I have a collection of alerts and I want to group it based on similarity/distance. As we have non-numeric data, How can i perform clustering for this kind of data.

  set.seed(42)   
  data.frame(Host1 = rep("del",10), 
  Host2 = c(rep("cpp",4), rep("sscp",3), rep("portal",3)),
 Host3 = c(rep("web",5), rep("apache",3), rep("app",2)), 
 Host4 = c(sample(3,8, replace = TRUE), rep("con",2)), 
 Date1 = abs(round(1:10 + rnorm(10),2))) 



   Host1  Host2  Host3 Host4 Date1
1    del    cpp    web     3  1.40
2    del    cpp    web     3  1.89
3    del    cpp    web     1  4.51
4    del    cpp    web     3  3.91
5    del   sscp    web     2  7.02
6    del   sscp apache     2  5.94
7    del   sscp apache     3  8.30
8    del portal apache     1 10.29
9    del portal    app   con  7.61
10   del portal    app   con  9.72

Looking forward to build clusters.

Answer 1

K-means only works for numerical (continuous) data

By definition, it minimizes squared deviations. Minimizing squared deviations only make sense on continuous data. Any kind of one-hot-encoding is only a hack; it makes the data types compatible, but not the approach sensible.

What is your similarity / distance?

Hierarchical clustering would work. If you can define a meaningful distance function that quantifies distance. But this is application dependant. We do not have your data, and do not understand your problem. We cannot solve this for you.