Optimised fuzzy C-means clustering based cluster indexing towards incremental learning integrated text categorisation
by Mamta Kayest
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 16, No. 2, 2023

Abstract: Text classification is the most significant task in the data retrieval process through classifying text into various groups depending on the document's content. The quick progression of electronic documents may produce various issues, such as unstructured data, which requires more effort and time for searching appropriate documents. Text categorisation has high importance in information retrieval and processing, wherein unstructured documents are arranged into a predefined group. In addition, incredible growth in online documents obtains ability with the development of the internet needs a highly precise and effective retrieval approach. Thus, in this paper, Dingo Monarch butterfly optimisation (DMBO) approach and Tversky index-based indexing are developed for incremental learning-enabled text categorisation. Moreover, text categorisation is done based on incremental learning along with a Bayesian classifier. This text classification approach achieved better performance with a precision of 0.9136, recall of 0.9173, F-measure of 0.9051, and accuracy of 0.8461.

Online publication date: Fri, 09-Jun-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com