Abstract

• As the amount of data volumes grows, the increasing dimensionality of datasets poses significant challenges for data analysis and interpretation. Dimensionality reduction techniques play a critical role by selecting or synthesizing essential dimensions to manage heterogeneous data regardless of structures (structured or unstructured) effectively. This research explores various dimensionality reduction methods, applied to multiple datasets, to enhance analytical insights and visualization during the exploratory phase.