This thesis investigates the implementation of a clustering algorithm on philosophical sentences from a variety of texts which belong to different schools of thought, where the goal is to have the resulting clusters mimic the already categorized sentences with their school of thought. The introductory chapter explains the importance of text mining as well as the motivation behind the writing of the thesis. After that, some background information, as well as the theoretical knowledge needed to pursue the implementation was defined, where fields such as text analysis, NLP, word representation techniques, and clustering were included. After that, the procedure that was done for the implementation and execution of the data pre-processing was described, as well as the statistics before and after the pre-processing, and also how the word representation techniques were implemented. Then the thesis presents the results of the clustering categorization combined with the comparison of the different word representation techniques by utilizing a clustering validation method. Subsequently, based on the results, a discussion chapter was written that brought up the reasons behind the results as well as the evaluation for further improvements. After that, the final summarization and conclusion of the research was presented.