In this paper, we present an effective way of identifying number of clusters k based on density of data in given dataset and optimality of clusters formed. We have used internal evaluation of clustering to choose optimal set of clusters after narrowing the selection space to a small ideal range. This range is identified by partitioning dataset into a number of partitions using kd-tree so that partitions created contains densely packed data objects. We have used the concept of multi-representative points of a cluster in partitioning and evaluation of clustering and implemented it by modifying CURE algorithm. In this paper, linear transformation method PCA is applied to reduce high dimensional data into lower dimensions.
['Alka Tripathi', 'Kirtee Panwar']
Modified CURE algorithm with enhancement to identify number of clusters