CFP last date
22 April 2024
Reseach Article

Optimal Search of Centroid for Open Source Intelligence Purpose using Partitioning Algorithm

Published on May 2015 by Mohd. Shajid Ansari, Manuraj Jaiswal
National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
Foundation of Computer Science USA
ACEWRM2015 - Number 2
May 2015
Authors: Mohd. Shajid Ansari, Manuraj Jaiswal
bdb59d9a-f242-4873-be08-4039627542c2

Mohd. Shajid Ansari, Manuraj Jaiswal . Optimal Search of Centroid for Open Source Intelligence Purpose using Partitioning Algorithm. National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering. ACEWRM2015, 2 (May 2015), 23-26.

@article{
author = { Mohd. Shajid Ansari, Manuraj Jaiswal },
title = { Optimal Search of Centroid for Open Source Intelligence Purpose using Partitioning Algorithm },
journal = { National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering },
issue_date = { May 2015 },
volume = { ACEWRM2015 },
number = { 2 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 23-26 },
numpages = 4,
url = { /proceedings/acewrm2015/number2/20907-6032/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
%A Mohd. Shajid Ansari
%A Manuraj Jaiswal
%T Optimal Search of Centroid for Open Source Intelligence Purpose using Partitioning Algorithm
%J National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
%@ 0975-8887
%V ACEWRM2015
%N 2
%P 23-26
%D 2015
%I International Journal of Computer Applications
Abstract

In internet billions amount of information published through no of ways like web pages, social media and website. Searching and analyzing these data is very complex task. Using partitioning algorithm for open source intelligence purpose optimal search can be implemented which help to convert unstructured data to structured data and also analysis and extraction the information significantly. In partitioning algorithm we use binary search technique. Each algorithm has its own advantages, limitations and shortcomings. Therefore, introducing novel and effective approaches for data clustering is an open and active research area. The binary search algorithm for data clustering that not only finds high quality clusters but also converges to the same solution in different runs. Open Source Intelligence (OSINT) aims at presenting valuable information based on publicly available data. As it might be expected, the Internet is a primary example of such data source. By applying text mining tools on a myriad of available services: online news, blogs, mailing lists, forums, portals, and a great amount of insight might be provided into almost any topic.

References
  1. CLUO: WEB – SCALE TEXT MINING SYSTEM FOR OPEN SOURCE INTELLIGENCE PURPOSE COMPUTER SCIENCE 14 (1) 2013
  2. Cover T. , Thomas J. : Elements of Information Theory. Wiley, 1991.
  3. Jurafsky D. , Martin J. H. : Speech and Language Processing Prentice Hall, 2 ed. 2008.
  4. Manning C. , Raghavan P. , Sch¨utze H. : Introduction to Information Retrieval. Cambridge University Press, 1 ed. , 2008.
  5. Fielding R. T. : Architectural styles and the design of network-based software architectures. PhD thesis, 2000.
  6. S. Z. Selim, K. Alsultan, A simulated annealing algorithm for the clustering problem, Pattern Recognition 24 (10) (1991) 1003–1008.
  7. K. S. Al-Sultan, A Tabu search approach to the clustering problem, Pattern Recognition 28 (9) (1995) 1443–1451.
  8. A. K. Qin,P. N. Suganthan, Kernel neural gas algorithms with application to cluster analysis, in: Proceedings—International Conference on Pattern Recognition, 2004.
  9. P. S. Shelokar, V. K. Jayaraman, B. D. Kulkarni, An ant colony approach for clustering, Analytica Chimica Acta 509 (2) (2004) 187–195.
  10. D. Karaboga, C. Ozturk, A novel clustering approach: artificial bee colony (ABC) algorithm, Applied Soft Computing 11 (1) (2011) 652–657.
  11. M. Fathian, B. Amiri, A. Maroosi, Application of honey-bee mating optimization algorithm on clustering, Applied Mathematics and Computation 190 (2) (2007) 1502–1513
Index Terms

Computer Science
Information Sciences

Keywords

Text Mining Osint K – Means Algorithm Agglomerative Algorithm.