ThetA -- fast and robust clustering via a distance parameter

Based on this, one can further divide distance-based methods into three categories: 1) assuming number of clusters as Clustering is a fundamental problem in machine known in advance, 2) a distance threshold as known or 3) learning where distance-based approaches have by assuming a limiting number of data points belonging to dominated the field for many decades. This set each particular cluster. of problems is often tackled by partitioning the data into K clusters where the number of clusters While clustering algorithms primarily focus on accurately is chosen apriori. While significant progress has partitioning the data, they also aimed at inferring information been made on these lines over the years, it is well from a data exploration standpoint. In this work, we established that as the number of clusters or dimensions primarily focus on distance-based clustering given its broad increase, current approaches dwell in adoption and propose a new framework, ThetA, which uses local minima resulting in suboptimal solutions.