A new approach for data clustering based on granular computing

Abstract

This paper introduces a new clustering technique based on granular computing. In traditional clustering algorithms, the integration of the high shaping capability of the existing datasets becomes fussy which in turn results in inferior functioning. Furthermore, the laid-out technique will be able to avoid these challenges through the use of granular computing to bring in a more accurate and prompt clustering process. The creation of a novel algorithm hinges on utilizing granules, which are the information chunks that reveal a natural structure as part of the data and also help with natural clustering. A testing of the algorithm's features is carried out by using state-of-the-art datasets and then an algorithm's effectiveness is compared to the other clustering methods. The results of the experiment show significant improvement in clustering accuracy and reduction in data analysis time, thus testifying how granular computing is efficient in data analysis. This quest is not only going to serve as a reinforcement in data clustering, but it will also probably be an input in the broader area of unsupervised learning, reinforcing positions for scalable and interpretable solutions for data-driven decision-making.