# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 2 Related Works
### Local Search Techniques  
In terms of increasing the amount of text information, it has become extremely difficult to deal with text information. Text clustering is an effective tool employed to manage a large number of text documents by classifying such text documents into clusters. In the end, it is challenging to cluster text documents with sparse, non-uniform distribution and uninformative features. The selection of the text function is a key unsupervised learning approach used to select a new subset of information text features. In Reference [71], a novel technique is suggested in based on the \(\beta\)-hill climbing technique for the problem of selecting text features to enhance text clustering (B-FSTC). The performance of the introduced \(\beta\)-hill climbing and original Hill climbing (i.e., H-FSTC) method are analyzed and compared with other techniques using k-mean text clustering. Experiments were carried out on four typical text datasets with different characteristics. Intriguingly, by generating a new subset of information text features, the suggested \(\beta\)-hill climbing method verifies better outcomes in comparison with the other techniques. Finally, to accomplish more accurate clusters, the \(\beta\)-hill climbing-based feature selection approach supports the k-mean clustering algorithm. In Reference [72], a new local search strategy, called \(\beta\)-hill climbing technique is presented to tackle the text document clustering issue. The \(\beta\)-hill climbing technique's main invention is \(\beta\). A balanceamong local and global search has been implemented. Local search (exploitation) techniques are applied effectively as the k-mean to the issue of text document clustering. Experiments were carried out on five randomly taken benchmark text datasets from \"Dmoz-Business\" datasets with different features. The findings demonstrate that, compared to the original hill climbing technique calculated by F-measure, precision, recall, and accuracy, the suggested \(\beta\)-hill climbing obtained better outcomes. The results indicate that by tuning the parameter of the \(\beta\)-hill claiming, the suggested (\(\beta\)-hill climbing) achieved better results compared to other original technique. In Reference [73], a cognitive-inspired multi-objective automated document clustering framework is suggested, which is a combination of the self-organizing map (SOM) and multi-objective differential evolution approach. In different population solutions, the variable number of cluster centers is encrypted in calculating the number of clusters from the dataset in an automated process. During evolution, these solutions undergo different genetic operations. In developing new genetic algorithm for the suggested clustering strategy, the idea of SOM is used. Two cluster validity measures, the Pakhira-Bandyopadhyay-Maulik index, and the Silhouette index, are optimized simultaneously to calculate the performance of a clustering solution. The efficiency of the introduced approach, including self- organizing map based multi-objective document clustering technique (SMODoc clust) is seen in automatic classification of some scientific papers and web-documents. The findings obtained clearly indicate that the proposed strategy is better than current strategies. The validation of the findings obtained is also shown by means of statistically relevant t-tests. In Reference [74], a new local clustering technique, called \(\beta\)-hill climbing, have been proposed to address the issue of the clustering of text documents by modeling the \(\beta\)-hill climbing method to partition related documents into the same cluster. The key innovation in the \(\beta\)-hill climbing technique is the \(\beta\) factor. It was implemented to manage local and global search. In order to overcome the issue of text documents clustering such as k-medoid and k-mean techniques, local search techniques are effectively used. Experiments have been carried out using eight text datasets with different features.