Text analysis in the field of text mining requires complex techniques for handling several text documents. Text clustering is among the most effective tactics in the field of text mining, machine recruitment and pattern recognition. Computers can start organizing a corpus document in certain organizational structure of conceptual clusters using reasonable text-clustering method. Informative and un-informational functionalities of the text documents contain noisy, inconsequential and superfluous features. The main method of finding a new subset of informative feats for each document is the unsupervised selection of text features. The functional selection technique has two aims: (1) maximize text clustering algorithm reliability, (2) minimize the number of uninformative traits. The proposed technique is that it produces a mature convergence rate and requires minimal computational time and is trapped in local minima in a low dimensional space. The text data is fed as the input and pre-processing steps are performed in the document. Next, the text feature selection is processed by selecting the local optima from the text document and then selecting the best global optima from local optimum using hybrid GWO–GOA.​ Furthermore, the selected optima are clustered using the Fuzzy c-means (FCM) clustering algorithm. This algorithm improves the reliability and minimizes the computational time cost. Eight datasets are used in the proposed algorithm and the performance is envisaged efficaciously. The evaluation metrics used for performing text feature selection and text clustering are accuracy, precision, recall, F-measure, sensitivity, specificity and show better quality when comparing with various other algorithms. When comparing with GWO, GOA and the proposed hybrid GWO–GOA algorithm, the proposed methodology reveals 87.6% of efficiency.

文本挖掘领域中的文本分析需要复杂的技术来处理多个文本文档。文本聚类是文本挖掘，机器募集和模式识别领域中最有效的策略之一。计算机可以使用合理的文本聚类方法开始在概念性群集的某些组织结构中组织语料库文档。文本文档的信息性和非信息性功能包含嘈杂，无关紧要和多余的功能。为每个文档找到新的信息专长子集的主要方法是对文本特征的无监督选择。功能选择技术有两个目标：（1）最大化文本聚类算法的可靠性，（2）最小化非信息特征的数量。所提出的技术是，它产生了成熟的收敛速度，并且需要最少的计算时间，并且被困在低维空间中的局部最小值中。文本数据将作为输入文件输入，并在文档中执行预处理步骤。接下来，通过从文本文档中选择局部最优值，然后使用混合GWO-GOA从局部最优值中选择最佳全局最优值来处理文本特征选择。此外，使用Fuzzy C-均值（FCM）对所选最优值进行聚类聚类算法。该算法提高了可靠性并最小化了计算时间成本。提出的算法中使用了八个数据集，并且有效地设想了性能。用于执行文本特征选择和文本聚类的评估指标包括准确性，准确性，召回率，F量度，与其他各种算法相比，具有更高的灵敏度，特异性和更好的质量。与GWO，GOA和提出的混合GWO-GOA算法进行比较时，提出的方法显示出87.6％的效率。