Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

413 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Hybrid clustering analysis using improved krill herd algorithm (2018, methodology): “15 β-hill climbing βHC 2017 [81] 16 K-mean clustering with PSO feature selection H-FSPSOTC 2017 [61] 17 Link based particle swarm optimization LBPSO 2017 [82] 18 A combination method of GSA and K-harmonic means GSA-KHM 2017 [64]”
Salp swarm algorithm: a comprehensive survey (2019, background): “…in optimization ﬁelds have survived active and got promising results due to its real close to all problems (i.e., industry-type problems, mathematical problems, real-world problems, and other problems) that belong to the category of hard optimization problems called NP-hard in nature [3–6].”
Moth–flame optimization algorithm: variants and applications (2019, background): “Examples of swarm-based algorithms include particle swarm optimization (PSO) [4], krill herd algorithm (KHA) [8], the salp swarm algorithm (SSA) [79], and the moth–ﬂame optimization (MFO) [78].”