  The objectives of this paper are to explore ways to analyze breast cancer
dataset in the context of unsupervised learning without prior training model.
The paper investigates different ways of clustering techniques as well as
preprocessing. This in-depth analysis builds the footprint which can further
use for designing a most robust and accurate medical prognosis system. This
paper also give emphasis on correlations of data points with different standard
benchmark techniques. Keywords: Breast cancer dataset, Clustering Technique
Hopkins Statistic, K-means Clustering, k-medoids or partitioning around medoids
(PAM)
