I am trying to identify habitat types from 85 plots. I intend to do a cluster analysis to identify habitat types, and hope to fit additional plots into the identified clusters.
(For context, I took measures from habitat plots in several different habitat types across a study site, then also measured the same variables at animal locations. I hope to identify differential habitat selection by two different species.)
Do I need to apply transformations to the data before doing cluster analysis?
- My data set includes categorical (eg. substrate type: mud, gravel, etc), euclidean distance (0 - 3400 cm), calculated indices (0 - 1.0), and vegetation percent cover (0 - 100, with lots of zeros) variables. Each of these would require different transformations to meet assumptions with other modelling methods, but what about when clustering?? Is each type considered on its own scale? Also, some of my variables are collinear - should these be removed before cluster analysis as with other methods?
Hierarchical or K-means methods?
- I had intended to use Gower dissimilarity matrix for a hierarchical cluster analysis, but is there an obvious reason to use K-means methods? I'm wondering if my choice of method here will impact my ability to 'fit' additional datapoints.
I am using R.