Clustering with Polar Coordinates System: Exploring Possibilities

Abstract

Clustering is unsupervised learning technique to group similar objects. The quality of clustering is assessed by several internal as well as external measures such as Dunn index, Davies–Bouldin index (DB), Calinski-Harabasz index (CH), Silhouette index, R-Squared, Rand, Jaccard, Purity and Entropy, F-measures, and many more. Researchers are exploring different approaches to improve quality of clustering by experimenting with different partitioning strategies (similarity/distance formula), by changing representation of data points or by applying different algorithms. In our earlier research paper (Joshi and Patil in 2016 IEEE Conference on Current Trends in Advanced Commuting (ICCTAC), pp 1–7, 2016 [1]), we put forth our observations of changing coordinate system of objects from Euclidean to Polar on clustering. In continuation, we further experimented to explore the possibilities of clustering with different distance techniques for partitioning objects represented in Polar coordinate system. We experimented with a standard as well as real data set. The quality of clustering is evaluated using Silhouette internal evaluation measure.