  Keeping in consideration the high demand for clustering, this paper focuses
on understanding and implementing K-means clustering using two different
similarity measures. We have tried to cluster the documents using two different
measures rather than clustering it with Euclidean distance. Also a comparison
is drawn based on accuracy of clustering between fuzzy and cosine similarity
measure. The start time and end time parameters for formation of clusters are
used in deciding optimum similarity measure.
