Year: 2025
Type: article
Abstract: Large amounts of data must be accessible in order to be properly organised and grouped so that access to the data is made easier; clustering algorithms are now doing this for us.Semantic data clustering, which is necessary for semantic interpretation of the input data, has received particular attention in recent years.Three modified clustering approaches are applied in this article, and their outcomes are assessed.On the basis of this, a method is initially created that applies a few principles to avoid confusion inside clusters.The provided data can be subjected to a rule-based clustering.The following method then applies ontology-based semantics to carry out these rules.Finally, the fundamental method modifies the assumed ontology before applying rules to clusters.The outcome demonstrates that the clusters formed from the data included within them were both highly similar and highly dissimilar from one another.Additionally, there was a notable decrease in the k-distance of these clusters, and the correlation was raised.
Author: Li shenghui L
Institution: Fudan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409459742