Generally, Text mining indicates the process of extracting maximum-quality information from the text. Moreover, it is mostly exploited in applications such as text categorization, text clustering, and text classification and so forth. In recent times, the text clustering is considered as the facilitating and challenging task exploited to cluster the text document. Because of the few inappropriate terms and large dimension, accuracy of text clustering is reduced. In this work, the semantic word processing and Enhanced CSO algorithm are presented for automatic text clustering. At first, input documents are stated as input to the preprocessing step that provides the useful keyword for clustering and feature extraction. After that, the ensuing keyword is applied to wordnet ontology to discover the hyponyms and synonyms of every keyword. Then, the frequency is determined for every keyword used to model the text feature library. Since it comprises the larger dimension, the entropy is exploited to choose the most significant feature. Hence, the proposed approach is exploited to assign the class labels to generate different clusters of text documents. The experimentation outcomes and performance is examined and compared with conventional algorithms such as ABC, GA, and PSO.
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