A Text Clustering Method Based On The Improved Heuristic Three-parent Genetic Algorithm

In the text clustering, the traditional clustering algorithm is easy to fall into the local optimal for exploring the clustering center, which will affect the results of text clustering. This paper proposes a text clustering method based on the improved heuristic three-parent genetic algorithm. In this paper, the heuristic three-parent genetic algorithm is improved. Furthermore, by enhancing the crossover strategy and introducing the unequal crossover, the genetic algorithm’s convergence speed and global search ability are improved. In text clustering, three-parent with the improved heuristic genetic algorithm to find the optimal clustering center. Through the change of fitness function, the clustering center is more suitable for text clustering results. Using the clustering center guide text embedded to lower-dimensional coding of high-dimensional text, finally use the Deep Embedded Clustering (DEC) to improve the effect of clustering. The clustering results are evaluated in terms of Accuracy and Mutual Information. The experimental results show that the algorithm performs well in selecting clustering centers and clustering results.