Solving text clustering problem using a memetic differential evolution algorithm.

ABSTRACT: The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution

SUBMITTER: Mustafa HMJ 

PROVIDER: S-EPMC7289410 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature