FSGA-MuRTe: evolutionary feature selection in multi-representational text environments for unsupervised text clustering

Each representation is independently L2-normalized and concatenated into a unified feature space prior to evolutionary feature selection. Feature subsets are optimized using a Genetic Algorithm guided by the Dunn Index as the fitness function, followed by unsupervised clustering using K-Means. Clustering quality is evaluated before and after feature selection using internal validation metrics, including Dunn Index (DI), Calinski–Harabasz Index (CH), and Davies–Bouldin Index (DB).

Experiments were conducted across multiple textual domains, including scientific abstracts, news articles, reviews, fake news datasets, sentiment analysis datasets, and short-text corpora obtained from publicly available sources. The datasets were previously processed, filtered, cleaned, and sampled to support reproducible clustering experiments in heterogeneous text environments. The results demonstrated consistent improvements in cluster compactness and separation after evolutionary feature selection, indicating that bioinspired optimization is an effective strategy for mitigating redundancy and improving clustering quality in multi-representational text environments.