Text Document Clustering Using the Hybrid Fruit Fly Optimization Algorithm

The Internet produces vast amounts of text data, expanding quickly. The abundance of text data in the unstructured format makes it difficult to extract and analyze the pertinent data. A text-mining technique called text document clustering separates text-based documents into completely independent groupings; consider likenesses between documents inside a group while simultaneously considering fluctuations in happiness between documents from various clusters. Partitioning the variety of text data by evaluating the practicality of the data in the documents is one of the issues tested in text clustering.

I. Introduction

Several academic disciplines have been paying close attention to optimal problems [1]–[4], and [5]. Many intelligence algorithms, including the taboo search, simulated annealing, differential evolution, and genetic algorithm (GA), have been used to tackle challenging improvement problems. These PC advances support progress and accomplishment in both examination and business. Swarm intelligence algorithms have gained greater attention recently because of their effectiveness and simplicity. They imitate the group conduct of social creatures to finish an optimization. Many practical problems have been addressed using the particle swarm optimization method (PSO) [6], [7], the ant colony optimization approach [8], and countless other insight techniques.