A Review of Clustering Methods forming Non-Convex clusters with, Missing and Noisy Data - Bhargav, Sushant; Pawar, Mahesh (International Journal of Computer Sciences and Engineering, 2025)
Clustering problem is among the foremost quests in Machine Learning Paradigm. The Big Data sets, being versatile, multisourced & multivariate, could have noise, missing values, & may form clusters with arbitrary shape. Because of unpredictable nature of Big Data Sets, the clustering method should be able to handle missing values, noise, & should be able to make arbitrary shaped clusters. The partition based methods for clustering does not form non-convex clusters, The Hierarchical Clustering Methods & Algorithms are able to make arbitrary shaped clusters but they are not suitable for large data set due to time & computational complexity. Density & Grid Paradigm do not solve the issue related to missing values. Combining different Clustering Methods could eradicate the mutual issues they have pertaining to dataset’s geometrical and spatial properties, like missing data, non-convex shapes, noise etc.