<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Textual outlier detection with an unsupervised method using text similarity and density peak

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

Textual outlier detection with an unsupervised method using text similarity and density peak

Online veröffentlicht: 08. Aug. 2023

Seitenbereich: 91 - 110

Eingereicht: 05. Mai 2023

DOI: https://doi.org/10.2478/ausi-2023-0008

Schlüsselwörter
outlier detection, text data processing, unsupervised learning

© 2023 Mahnaz Taleb Sereshki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Textual outlier detection with an unsupervised method using text similarity and density peak

Online veröffentlicht: 08. Aug. 2023

Seitenbereich: 91 - 110

Eingereicht: 05. Mai 2023

DOI: https://doi.org/10.2478/ausi-2023-0008

Schlüsselwörteroutlier detection, text data processing, unsupervised learning

© 2023 Mahnaz Taleb Sereshki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
outlier detection, text data processing, unsupervised learning