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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:19:42Z","timestamp":1741753182594,"version":"3.38.0"},"reference-count":37,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2023,11,20]]},"abstract":"The text clustering model becomes an essential process to sort the unstructured text data in an appropriate format. But, it does not give the pave for extracting the information to facilitate the document representation. In today\u2019s date, it becomes crucial to retrieve the relevant text data. Mostly, the data comprises an unstructured text format that it is difficult to categorize the data. The major intention of this work is to implement a new text clustering model of unstructured data using classifier approaches. At first, the unstructured data is taken from standard benchmark datasets focusing on both English and Telugu languages. The collected text data is then given to the pre-processing stage. The pre-processed data is fed into the model of the feature extraction stage 1, in which the GloVe embedding technique is used for extracting text features. Similarly, in the feature extraction stage 2, the pre-processed data is used to extract the deep text features using Text Convolutional Neural Network (Text CNN). Then, the text features from Stage 1 and deep features from Stage 2 are all together and employed for optimal feature selection using the Hybrid Sea Lion Grasshopper Optimization (HSLnGO), where the traditional SLnO is superimposed with GOA. Finally, the text clustering is processed with the help of Deep CNN-assisted hierarchical clustering, where the parameter optimization is done to improve the clustering performance using HSLnGO. Thus, the simulation findings illustrate that the framework yields impressive performance of text classification in contrast with other techniques while implementing the unstructured text data using different quantitative measures.<\/jats:p>","DOI":"10.3233\/idt-220201","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T15:13:30Z","timestamp":1694790810000},"page":"1323-1350","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid unstructured text features for meta-heuristic assisted deep CNN-based hierarchical clustering"],"prefix":"10.1177","volume":"17","author":[{"given":"Bankapalli","family":"Jyothi","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, JNTUK Kakinada, Kakinada, Andhra Pradesh, India"}]},{"given":"L.","family":"Sumalatha","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India"}]},{"given":"Suneetha","family":"Eluri","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, JNTUK Kakinada, Kakinada, Andhra Pradesh, India"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IDT-220201_ref1","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TKDE.2011.205","article-title":"Clustering Sentence-Level Text Using a Novel Fuzzy Relational Clustering Algorithm","volume":"25","author":"Skabar","year":"2013","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/IDT-220201_ref2","doi-asserted-by":"crossref","first-page":"92037","DOI":"10.1109\/ACCESS.2019.2927345","article-title":"Discovering Topic Representative Terms for Short Text Clustering","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"issue":"10","key":"10.3233\/IDT-220201_ref3","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/TKDE.2009.174","article-title":"An Efficient Concept-Based 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