Publishers are getting content frequently as demand of publication increases day by day. To resolve an issue of identifying the research paper class as per content this work proposed a hybrid model. Features were select by the fish schooling genetic algorithm and indexing was provide by hash structure. In order to maintain the privacy of the user and server data model work on key based searching of relevant document. Each document has set of keywords and each keyword has its own unique key. So user query pass as set of unique keys and searching of cluster document was done by matching keys with hash index. Experiment was done on real dataset having set of document from different field of publication. Result shows that proposed model FSGA has increases the result outcome by fetching more relevant text documents as per user query.

Clustering, Genetic Algorithm, Text Mining, Pattern Feature

[1]. Abhishek Jain, Aman Jain, Nihal Chauhan, Vikrant Singh and Narina Thakur. “Information Retrieval using Cosine and Jaccard Similarity Measures in Vector Space Model”. International Journal of Computer Applications 164(6):28-30, April 2017.
[2]. Dr.M.Suresh Babu, Mr. A.Althaf Ali, Mr. A.Subramaneswara Rao, “A Study on Information Retrieval Methods in Text Mining”, International Journal Of Engineering Research & Technology (Ijert) Ncdma, Volume 2 – Issue 15, 2014
[3]. P, Mrs. (2020). A Prognostic Rainfall using Machine Learning Technique. International Journal for Research in Applied Science and Engineering Technology. 8: 1 2020.
[4]. Wu Chuhan, et al. A hybrid unsupervised method for aspect term and opinion target extraction Knowledge-Based Systems, 148, 2018.
[5]. Giannakopoulos, Athanasios "Unsupervised aspect term extraction with b-lstm & crf using automatically labelled datasets." Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2017.
[6]. Zhitao Guan, Xueyan Liu, Longfei Wu, Jun Wu, Ruzhi Xu, Jinhu Zhang, Yuanzhang Li, Cross-lingual multi-keyword rank search with semantic extension over encrypted data, Information Sciences, Volume 514, 2020.
[7]. Jeong, Soyeong and Baek, Jinheon and Park, ChaeHun and Park, Jong. "Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation". Proceedings of the Second Workshop on Scholarly Document Processing, 2021.
[8]. H. Chiranjeevi and K. S. Manjula, "An Text Document Retrieval System for University Support Service on a High Performance Distributed Information System," 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2019.
[9]. Soyeong Jeong, Jinheon Baek, ChaeHun Park, Jong C. Park. "Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation". Proceedings of the Second Workshop on Scholarly Document Processing, pages 7–17 June 10, 2021.
[10]. Tuyen Thi-Thanh Do; Dang Tuan Nguyen. "A computational semantic information retrieval model for Vietnamese texts" International Journal of Computational Science and Engineering Vol.24 No.3., 2021
[11]. https://ijsret.com/2017/12/14/computer-science/
[12]. Alan Díaz-Manríquez , Ana Bertha Ríos-Alvarado, José Hugo Barrón-Zambrano, Tania Yukary Guerrero-Melendez, And Juan Carlos Elizondo-Leal. “An Automatic Document Classifier System Based on Genetic Algorithm and Taxonomy”. accepted March 9, 2018, date of publication March 15, 2018, date of current version May 9, 2018.
[13]. Vinod Sharm, “Document Class Identification Using Fire-Fly Genetic Algorithm and Normalized Text Features” Volume 6 Issue 1, ijsret.com.
[14]. Vinod Sharma, Dr. Shiv Shakti Shrivastava and Dr. Sanjeev Kumar Gupta.“Fish Schooling Genetic Algorithm for Text document Clustering Using Pattern Features”. International Journal of Grid and Distributed Computing (I.J.G.D.C.) Vol. 13, No. 1, 2020.