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

TLDR

The rapid growth of the Internet generates massive, unstructured text data, making it difficult to extract and analyze information, and text document clustering seeks to partition documents into similarity‑based clusters, a task complicated by measuring content relevance. The study proposes a hybrid swarm intelligence algorithm combined with K‑means to improve text clustering. The hybrid fruit‑fly optimization algorithm was first benchmarked on ten unconstrained CEC2019 functions and then applied to six standard text datasets, integrating K‑means for clustering. Results show the hybrid method outperforms state‑of‑the‑art techniques on both benchmark optimization functions and standard text clustering datasets, demonstrating robustness and superiority.

Abstract

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.