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

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 docum...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mathematics (Basel) Ročník 9; číslo 16; s. 1929
Hlavní autoři: Bezdan, Timea, Stoean, Catalin, Naamany, Ahmed Al, Bacanin, Nebojsa, Rashid, Tarik A., Zivkovic, Miodrag, Venkatachalam, K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2021
Témata:
Algorithms
Benchmarks
Clustering
Cooperation
Data mining
Evaluation
Feature selection
Food science
Foraging behavior
fruit-fly optimization algorithm
Hybrid systems
Intelligence
K-means
machine learning
metaheuristic algorithms
Neural networks
Optimization
Optimization algorithms
Population
Swarm intelligence
text document clustering
Unstructured data
ISSN:2227-7390, 2227-7390
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math9161929