I am working on a dataset to identify clusters among people based on their ratings on Likert scale(1-5) i.e Strongly disagree - Strongly agree, consist of 1000 observations and 19 features, all measured on the same scale. I am trying to find answers to the following questions:
a) Is normalization necessary/mandatory before measuring dissimilarity?
b) What is the similarity/dissimilarity metric to be applied here to perform hierarchical clustering? viz - euclidean, manhattan, gower…etc what is correlation based distance measure means?
c) Is kmeans function in R able to cluster with default metrics? if not then what is the alternative?
d) What is the best way to perform the same in R?