CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

A New Approach to Automated Summarization based on Fuzzy Clustering and Particle Swarm Optimization

by Anshita, Rahul Kumar Yadav, Sugandha Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 1
Year of Publication: 2016
Authors: Anshita, Rahul Kumar Yadav, Sugandha Singh
10.5120/ijca2016910972

Anshita, Rahul Kumar Yadav, Sugandha Singh . A New Approach to Automated Summarization based on Fuzzy Clustering and Particle Swarm Optimization. International Journal of Computer Applications. 148, 1 ( Aug 2016), 12-15. DOI=10.5120/ijca2016910972

@article{ 10.5120/ijca2016910972,
author = { Anshita, Rahul Kumar Yadav, Sugandha Singh },
title = { A New Approach to Automated Summarization based on Fuzzy Clustering and Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 1 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number1/25720-2016910972/ },
doi = { 10.5120/ijca2016910972 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:52:07.595331+05:30
%A Anshita
%A Rahul Kumar Yadav
%A Sugandha Singh
%T A New Approach to Automated Summarization based on Fuzzy Clustering and Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 1
%P 12-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automated Summarization of the text is now become an important aspect as it makes the meaning of documents easy to understand and easy to read. Automated summarization is the process of decreasing a text document with a computer system to be able to develop a synopsis that retains the main points associated with document this is certainly initial. Once the irritating dilemma of information overload is continuing to grow, and as the total amount of data has increased, so has fascination with automated summarization. A typical example of the application of summarization technology such as for example Bing and Document summarization is another. There are number of clustering algorithms which have been used in the past as clustering plays significant role in summarizing of the documents. In this paper, we discussed about the existing clustering algorithms. We also proposed a hybridized algorithm based on the combination of fuzzy C-Means and Particle Swarm Optimization. In the last, we compared our proposed algorithm results with the existing clustering algorithms.

References
  1. Magnus, Rosell, and Sumithra Velupillai. "The impact of phrases in document clustering for Swedish." In NoDaLiDa 2005, Joensuu, Finland, 2005, pp. 173-179. 2005.
  2. Michael, Steinbach, George Karypis, and Vipin Kumar. "A comparison of document clustering techniques." In KDD workshop on text mining, vol. 400, no. 1, pp. 525-526. 2000.
  3. Anton Leuski. "Evaluating document clustering for interactive information retrieval." In Proceedings of the tenth international conference on Information and knowledge management, pp. 33-40. ACM, 2001.
  4. Silva, Joaquim, Joao Mexia, Agra Coelho, and Gabriel Lopes. "Document clustering and cluster topic extraction in multilingual corpora." In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, pp. 513-520. IEEE, 2001.
  5. Benjamin Chin Ming Fung. "Hierarchical document clustering using frequent itemsets." PhD diss., SIMON FRASER UNIVERSITY, 2002.
  6. Yi, Peng, Gang Kou, Zhengxin Chen, and Yong Shi. "Recent trends in data mining (DM): Document clustering of DM publications." In Service Systems and Service Management, 2006 International Conference on, vol. 2, pp. 1653-1659. IEEE, 2006.
  7. Yulan, He, Siu Cheung Hui, and Alvis Cheuk M. Fong. "Mining a web citation database for document clustering." Applied artificial intelligence 16, no. 4 (2002): 283-302..
  8. Wei, Xu, Xin Liu, and Yihong Gong. "Document clustering based on non-negative matrix factorization." In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 267-273. ACM, 2003.
  9. Vladimir, Dobrynin, David Patterson, and Niall Rooney. "Contextual document clustering." In Advances in Information Retrieval, pp. 167-180. Springer Berlin Heidelberg, 2004.
  10. Jian, Zhang, Zoubin Ghahramani, and Yiming Yang. "A probabilistic model for online document clustering with application to novelty detection." In Advances in Neural Information Processing Systems, pp. 1617-1624. 2004.
  11. Soumi Ghosh and Sanjay Kumar Dubey,” Comparative Analysis of K-Means and Fuzzy CMeans Algorithms”, International Journal of Advanced Computer Science and Applications, Vol. 4, No.4, 2013.
  12. Y. Yong, Z. Chongxun and L. Pan, “A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding”, Measurement Science Review, vol. 4, no.1, 2004
  13. S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure”, IEEE Transactions on Systems, Man and Cybernetics, vol. 34, 1998, pp. 1907-1916.
  14. V. S. Rao and Dr. S. Vidyavathi, “Comparative Investigations and Performance Analysis of FCM and MFPCM Algorithms on Iris data”, Indian Journal of Computer Science and Engineering, vol.1, no.2, 2010 pp. 145-151.
Index Terms

Computer Science
Information Sciences

Keywords

Hybridized Clustering Particle Swarm Optimization Fuzzy C-Means

Powered by PhDFocusTM