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A Survey: Techniques of an Efficient Search Annotation based on Web Content Mining

by Sobana.e, Muthusankar.d
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 104 - Number 3
Year of Publication: 2014
Authors: Sobana.e, Muthusankar.d
10.5120/18181-9072

Sobana.e, Muthusankar.d . A Survey: Techniques of an Efficient Search Annotation based on Web Content Mining. International Journal of Computer Applications. 104, 3 ( October 2014), 12-16. DOI=10.5120/18181-9072

@article{ 10.5120/18181-9072,
author = { Sobana.e, Muthusankar.d },
title = { A Survey: Techniques of an Efficient Search Annotation based on Web Content Mining },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 104 },
number = { 3 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume104/number3/18181-9072/ },
doi = { 10.5120/18181-9072 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:35:11.599640+05:30
%A Sobana.e
%A Muthusankar.d
%T A Survey: Techniques of an Efficient Search Annotation based on Web Content Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 104
%N 3
%P 12-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the World Wide Web, or simply the web, the content of information is changing everyday and it is known as dynamic environment. There is more information are uploaded in web and it has grown steadily in recent years. Therefore the several billions of HTML documents, pictures and another multimedia files available on the Internet. Due to the overloaded of information in web, the information extraction is not effectively based on user needs. To overcome the above problem, there is a need of methods to help us extract information effectively from the content of web pages. Nowadays, various web content mining techniques are developed to mine the information and serve people in a simple way: These techniques focuses on the discovery/retrieval of the useful information from the Web contents/data/documents. This paper focus on how to extract the information effectively based on classification and clustering, and detecting phishing websites.

References
  1. Abdelhakim Herrouz, Chabane Khentout, Mahieddine Djoudi, 2013 "Overview of Web Content Mining Tools," The International Journal of Engineering And Science.
  2. Claudia Elena Dinuc?, Dumitru Ciobanu, 2012 "Web Content Mining," Annals of the University of Petro?ani, Economics, PP. 85-92.
  3. Danushka Bollegala,Yutaka Matsuo, and Mitsuru Ishizuka, 2013 "Minimally Supervised Novel Relation Extraction Using a Latent Relational Mapping," IEEE Transactions On Knowledge and Data Engineering, Vol. 25, No. 2, pp. 419-432.
  4. Darshna Navadiya, Roshni Patel, 2012 "Web Content Mining Techniques-A Comprehensive Survey," International Journal of Engineering Research & Technology," Vol. 1.
  5. Dayong Wang, Steven C. H. Hoi, Ying He, and Jianke Zhu, 2014 "Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation," IEEE Transactions On Knowledge and Data Engineering, Vol. 26, No. 1, pp. 166-179.
  6. Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu,Tao Mei, and Jiebo Luo, 2014 "Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding," IEEE Transactions On Pattern Aanalysis and Machine Intelligence, Vol. 36, No. 3, pp. 550-563.
  7. Govind Murari Upadhyay, Kanika Dhingra, 2013 "Web Content Mining: Its Techniques and Uses," Vol. 3, PP. 610-613.
  8. Haijun Zhang, Gang Liu, Tommy W. S. Chow, and Wenyin Liu, 2011 "Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach," IEEE Transactions on Neural Network, Vol. 22, No. 10, pp. 1532-1546.
  9. Hao Ma, Irwin King, and Michael Rung-Tsong Lyu, 2012 "Mining Web Graphs for Recommendations," IEEE Transactions On Knowledge and Data Engineering, Vol. 24, No. 6, pp. 1051-1064.
  10. Hassan A. Sleiman and Rafael Corchuelo, 2013 "A Survey on Region Extractors from Web Documents," IEEE Transactions On Knowledge and Data Engineering, Vol. 25, No. 9, pp. 1960-1981.
  11. Jaideep Srivastava, Prasanna Desikan, Vipin Kumar, "Web Mining - Concepts, Applications & Research Directions," Department of Computer Science, PP. 51-71.
  12. Jemma Wu, 2012 "A Framework for Learning Comprehensible Theories in XML Document Classification," IEEE Transactions On Knowledge and Data Engineering, Vol. 24, No. 1, pp. 1-14.
  13. Mohammad Khabbaz, Keivan Kianmehr, and Reda Alhajj, 2012 "Employing Structural and Textual Feature Extraction for Semistructured Document Classification," IEEE Transactions on Systems, Man, Cybernetics-PartC: Applications and Reviews, Vol. 42, No. 6, pp. 1556-1578.
  14. Niki R. Kapadia, Kinjal Patel, 2012 "Web Content Mining Techniques – A Comprehensive Survey," International Journal of Research in Engineering & Applied Sciences, pp. 1869-1877.
  15. Weiwei Zhuang, Yanfang Ye, Yong Chen, and Tao Li, 2012 "Ensemble Clustering for Internet Security Applications," IEEE Transactions On Systems, Man,and Cybernetics, Vol. 42, No. 6, pp. 1784-1796.
  16. Yan Wang, 2000 "Web Mining and Knowledge Discovery of Usage Patterns," CS 748T Project (Part I), PP. 1-25.
  17. Yi Yang, Fei Wu, Feiping Nie, Heng Tao Shen, Yueting Zhuang, and Alexander G. Hauptmann, 2012 "Web and Personal Image Annotation by Mining Label Correlation With Relaxed Visual Graph Embedding," IEEE Transactions On Image Processing, Vol. 21, No. 3, pp. 1339-1351.
Index Terms

Computer Science
Information Sciences

Keywords

Web Content mining classification clustering phishing Websites.

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