Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering

Timea Bezdan, Catalin Stoean, Ahmed Al Naamany, Nebojsa Bacanin, Tarik A. Rashid, Miodrag Zivkovic and K. Venkatachalam
Additional contact information
Timea Bezdan: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Catalin Stoean: Human Language Technology Research Center, University of Bucharest, 010014 Bucharest, Romania
Ahmed Al Naamany: Department for Mathematics and Computer Science, Modern College of Business and Science, Muscat 113, Oman
Nebojsa Bacanin: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Tarik A. Rashid: Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil 44001, Iraq
Miodrag Zivkovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
K. Venkatachalam: Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore 560029, India

Mathematics, 2021, vol. 9, issue 16, 1-19

Abstract: The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods.

Keywords: machine learning; text document clustering; metaheuristic algorithms; fruit-fly optimization algorithm; K-means (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/16/1929/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/16/1929/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:16:p:1929-:d:613680

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().