ABSTRACT
The difference between computers and the human brain is explained by reasoning. This means that the human brain can use uncertain data, but computers reason with precise data [1]. Nowadays, fuzzy logic has become an important solution to reduce the difference between the human brain and computers.
Fuzzy logic has become an important field of study, thanks to its ability to help researchers to manipulate data that were not accurate and not precise; it can manipulate vague propositions. But classical logic deals with exact values of variables, which means it only supports precise data; however, it cannot handle uncertain and imprecise data. In our work, we propose an approach based on fuzzy logic and Euclidean distance metric for text document clustering. The idea is to search for the similarities and dissimilarities between biological documents to facilitate classification.
