I have some data where I have certain classes (c1, c2, c3, c4 ...) and the data comprises of binary vectors where 1 and 0 denote that an entry belongs to a class or not. The number of classes will be > 200. The data will look like this:
c1 c2 c3 c4 ... 1 0 0 0 ... 0 1 1 0
Would this data come under "Categorical" type?
Sample Size : ~20000
No. of classes : 300
Data Matrix Sparsity : 99.52%
Problem Statement: The classes that I am talking about are medical services provided by Hospitals. If a hospital provides the service we just put 1 or else 0 in the binary vector. I want to cluster similar hospitals on the basis of their services.
I tried out PCA for dimension reduction on this dataset and I even got good clusters with DBSCAN but I read that for categorical sparse data PCA is not recommended and also Euclidian distance as the distance measure is not good.
I am planning to use MCA (Multiple Correspondence Analysis) but I cannot figure out how am I supposed to represent the data for that.