Title: Optimised fuzzy C-means clustering based cluster indexing towards incremental learning integrated text categorisation

Authors: Mamta Kayest

Addresses: Department of Computer Science and Engineering, Punjab Engineering College, Sector 12, Chandigarh 160012, India

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.

Keywords: Dingo optimiser; monarch butterfly optimisation algorithm; Bayesian classifier; fuzzy C-means clustering; Lin similarity; Dingo Monarch butterfly optimisation; DMBO.

DOI: 10.1504/IJIIDS.2023.131413

International Journal of Intelligent Information and Database Systems, 2023 Vol.16 No.2, pp.196 - 219

Received: 25 Jun 2022
Accepted: 19 Jan 2023
Published online: 09 Jun 2023 *

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