摘要：In today's society, information grows exponentially every day, resulting in a massive growth in its volume. The search engines are used to gather all of the information. The user-provided relevant words are mined by the search engines. People like to evaluate pertinent information while using. As a result, the most difficult challenge in today's society is to make a search engine efficient enough to locate the essential information. It's because individuals rely exclusively on the internet for their information needs. Many algorithms are used by web engines to carry out their search strategy. Many methods have been proposed by researchers to improve the use of web engines. Traditional techniques such as K means and K medoid clustering have led to various ideas being suggested by scholars. Other algorithms, referred to be bio inspired or nature inspired algorithms, are in opposition to these. There are bio-inspired algorithms that aid in the clustering of text content. This work proposes Text Document Clustering Using Optimized Cuckoo Search Algorithm (TDOCS) to cluster text documents. It is concerned with determining whether the new solution outperforms the older ones. To cluster the text document, the cuckoo search algorithm requires the statement of the cluster size. However, this paper's suggestion focuses on the operation of the optimised cuckoo search algorithm rather than cluster size. The optimised cuckoo search algorithm entails the automatic estimation of cluster size. This method maximises efficiency by reducing the amount of effort required to provide the required number of clusters.
