Survey on Efficient Techniques of Text Mining

EOI: 10.11242/viva-tech.01.01.07

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Sunita Naik, Samiksha Gharat, Saraswati Shenoy, Rohini Kamble, "Survey on Efficient Techniques of Text Mining", VIVA-Tech IJRI Volume 1, Issue 1, Article 7, pp. 1-7, Oct 2018. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


In the current era, with the advancement of technology, more and more data is available in digital form. Among which, most of the data (approx. 85%) is in unstructured textual form. So it has become essential to develop better techniques and algorithms to extract useful and interesting information from this large amount of textual data. Text mining is process of extracting useful data from unstructured text. The algorithm used for text mining has advantages and disadvantages. Moreover the issues in the field of text mining that affect the accuracy and relevance of the results are identified.


MWO, Consensus, PSO, Text mining, Bisecting K-means.


  1. Jing An, Qi Kang, Lei Wang, Qidi Wu "Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization" IEEE International Conference on Networking, Sensing and Control.
  2. Peng Yan, ShiYao Lui, Bing zyao Huang "A Data Clustering Algorithm Based on Mussels Wandering Optimization" IEEE International Conference 2014.
  3. Sonam Tripathi, Asst prof.Tripathi Sharma."A Survey Paper for Finding Frequent Pattern In Text Mining" International Journal of Advanced Research in Computer Engineering &Technology(IJRCET).
  4. Ahmed A. Abusnaina, Rosni Abdullah. "Mussels Wandering Optimization Algorithm Based Trainning of Artifical Neural Networks For Pattern Classification” International Conference on Computing and Information.(ICOCI)2013.
  5. Rashmi P. Dagde, Snehlata Dongre “A Review on Clustering Analysis based on Optimization Algorithm for Data mining”. IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017.
  6. Zhang Zhen, Chen Chao, Chen jun-liang “Algorithm of Group Members' consensus orienting to Discussion Dynamic Process”. IEEE Transaction.
  7. Victor Solo “Stability of Distributed Adaptive Algorithms I: Consensus Algorithms” IEEE Transaction 2015.
  8. Chiabwoot Ratanavilisagul and Boontee Kruatrachue “A Modified Particle Swarm Optimization with Dynamic Particles Reinitialization Period”. Springer International Publishing Switzerland 2014.
  9. Ramzan Talib, Muhammad kashif Mani, Shaeela Ayesha, Fakeeha Fatima, “Text Mining: Techniques, application and issues”, IJACSA(2016).
  10. Sarkar, Arindam Roy & B.S Purkayastha,” A comparative Analysis of particle swarm optimization and k-mean Algorithm for Text clustering using Nepali wordnet”, IJNLC(June 2014).
  11. Jayshree Ghorpade-Aher, Roshan Bagdiya,”Review on clustering web data using pso”, International Journal of computer application( December 2014).
  12. Yu Zhuang, YuMau, Xinchen, “A limited Iteration Bisecting k-means for fast clustering large datasets”, IEEE trust com(2016).
  13. Rekha Dahiya, Anshima Singh, “A survey on application of particle swarm optimization in Text Mining”, International Journal of Innovative research & development(May 2014).
  14. Nikita P. Katariya, Prof. M. S. Chaudhari “Bisecting K-means Algorithm for Text Clustering”. IJARCSSE February 2015.