Abstract
Text mining is an intriguing area of research, considering there is an abundance of text across the Internet and in social medias. Nevertheless outliers pose a challenge for textual data processing. The ability to identify this sort of irrelevant input is consequently crucial in developing high-performance models. In this paper, a novel unsupervised method for identifying outliers in text data is proposed. In order to spot outliers, we concentrate on the degree of similarity between any two documents and the density of related documents that might support integrated clustering throughout processing. To compare the e ectiveness of our proposed approach with alternative classification techniques, we performed a number of experiments on a real dataset. Experimental findings demonstrate that the suggested model can obtain accuracy greater than 98% and performs better than the other existing algorithms.