Integrating Decision Theory and Syntactic Data for Enhanced Rough Fuzzy C-Means Clustering Algorithm

Clustering is a basic strategy in data mining, allowing the detection of latent patterns within complex datasets. However, when dealing with the datasets composed of a variety of properties, traditional clustering algorithms frequently find challenges. Also, the data gets separated into different clusters in the hard clustering paradigm, with each data clearly associated with a specific cluster. On the other hand, Rough Clustering outperforms these constraints by allowing data points to belong to many clusters at the same time and form connections with components of other clusters. The incorporation of decision theory allows to offer an entirely new trajectory for the creation of clusters. This advancement intends to provide the clustering process with an inherent adaptive nature, allowing the algorithms to res pond to data patterns dynamically and optimize cluster allocations. This research study provides a coherent and systematic methodology for cluster evolution through the perspective of decision theory, thereby contributing to the advancement of clustering techniques.