This thesis aimed to study some findings related to pretopological spaces, explain their fundamental characteristics, develop some associated results, and apply them in practical data analysis applications.
The study addressed important influences in this field, most notably the pseudo-closure and interior influences, while also demonstrating the relationship between pretopological and classical topological spaces and highlighting the flexibility that pretopology offers in representing relationships between data elements.

The thesis also included an applied aspect through the use of the similarity matrix in the first application and the dissimilarity matrix in the second, with the aim of employing the pretopological methodology in analyzing different datasets and extracting important features and relationships within them.