Text Document Clustering Approach by Improved Sine Cosine Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50022533" target="_blank" >RIV/62690094:18470/23:50022533 - isvavai.cz</a>

Výsledek na webu

<a href="https://itc.ktu.lt/index.php/ITC/article/view/33536" target="_blank" >https://itc.ktu.lt/index.php/ITC/article/view/33536</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5755/j01.itc.52.2.33536" target="_blank" >10.5755/j01.itc.52.2.33536</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Text Document Clustering Approach by Improved Sine Cosine Algorithm

Popis výsledku v původním jazyce

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

Název v anglickém jazyce

Text Document Clustering Approach by Improved Sine Cosine Algorithm

Popis výsledku anglicky

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

INFORMATION TECHNOLOGY AND CONTROL

ISSN

1392-124X

e-ISSN

1392-124X

Svazek periodika

52

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

21

Strana od-do

541-561

Kód UT WoS článku

001091788500021

EID výsledku v databázi Scopus

2-s2.0-85168718410