Extermination of Obsolete Relationship Through KTMIN-JAK-MAXAM Algorithm in Confusion Mining
American Journal of Data Mining and Knowledge Discovery
Volume 4, Issue 1, June 2019, Pages: 19-23
Received: Feb. 25, 2019; Accepted: Apr. 10, 2019; Published: May 11, 2019
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Authors
Kittappa Thiagarajan, Academic Research and Development, Jeppiaar Engineering College, Chennai, India
Jeyavel Kavitha, Department of Mathematics, R&D Centre, Bharathiar University, Coimbatore, India
Karunakaran Sarukesi, KCG College Engineering and Technology, Chennai, India
Avudaiappan Maheshwari, Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, India
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Abstract
At the present time web contains many indistinguishable documents. Much effort made towards in investigates mechanism with identical detection algorithms, still the retrieved web documents with outmodedlink. In this proposed system, we are successfully identifying and minimize the redundant information and like link in web documents. We introduce the correct graph theory based KTMIN-JAK-MAXAM algorithm filters out the redundant link. From the proposed system, we have relevant information with more accuracy. Using this KTMIN-JAK-MAXAM algorithm accessing of web pages with reduced time and space complication.
Keywords
Data Mining, Degree, Maximum Degree, Minimum Degree, Link, Path
To cite this article
Kittappa Thiagarajan, Jeyavel Kavitha, Karunakaran Sarukesi, Avudaiappan Maheshwari, Extermination of Obsolete Relationship Through KTMIN-JAK-MAXAM Algorithm in Confusion Mining, American Journal of Data Mining and Knowledge Discovery. Vol. 4, No. 1, 2019, pp. 19-23. doi: 10.11648/j.ajdmkd.20190401.14
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
S. Poonkuzhali, K. Thiagarajan, K. Sarukesi, Set theoretical Approach for mining web content through outliers detection, International journal on research and industrial applications, Volume 2, Jan 2009.
[2]
Changjun Wu, GuosunZeng, GuorongXu, A Web Page Segmentation Algorithm for Extracting Product Information, Information Acquisition, 2006 IEEE International Conference on Publication Date: Aug. 2006.
[3]
Bing Liu, Kevin Chen- Chuan Chang, Editorial: Special issue on Web Content Mining, SIGKDD Explorations, Volume 6, Issue 2.
[4]
JaroslavPokorny, JozefSmizansky, Page Content Rank: An approach to the Web Content Mining.
[5]
Malik Agyemang Ken Barker Rada S. Alhajj, Mining Web Content Outliers using Structure Oriented Weighting Techniques and N-Grams, 2005 ACM Symposium on Applied Computing.
[6]
Ricardo Campos, Gael Dias, Celia Nunes, WISE: Hierarchical Soft Clustering of Web Page Search Results based on Web Content Mining Techniques, International conference on Web Intelligence, IEEE/WIC/ACM 2006.
[7]
Jiang Yiyong, Zhang Jifu, CaiJainghui, Zhang Sulan, Hu Lihua, The Outliers Mining Algorithm Based On Constrained Concept Lattice, Internal Symposium on Data Privacy and E.commerce, IEEE 2007.
[8]
Kshitija Pol, Nita Patil, ShreyaPatankar, Chhaya Das, A Survey on Web Content Mining and Extraction of Structured and Semistructureddata, First International Conference on Emerging trends in Engineering and Technology, 2008.
[9]
J. P. Tremblay and R. Manohar, “Discrete Mathematical Structures with Applications to Computer Science”, TMH, 1997.
[10]
D. C. Sancheti and E. K. Kapoor, Statistics (Theory, Methods & Application) By Published by Sultan Chand and Sons, Sixth thoroughly revised Edition, 1990.
[11]
N. P. Gopalan, J. Akilandeswari, Distributed, Fault-tolerant Multi-agent Web Mining System for Scalable Web Search 5th WSEAS Int. Conf. on APPLIED INFORMATICS and COMMUNICATIONS, Malta, September 15-17, 2005 (pp384-390).
[12]
G. Castellano, A. M. Fanelli, M. A. Torsello, Mining usage profiles from access data using fuzzy clustering, 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, September 22-24, 2006.
[13]
AnoopPaharia, YachanaBhawsar, YachanaBhawsar, Developing Web intelligence using data mining 6th WSEAS Int. Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, Tenerife, Spain, December 14-16, 2007.
[14]
Web Document Classification and its Performance Evaluation: IOAN POP, 9th WSEAS International Conference on EVOLUTIONARY COMPUTING (EC’08), Sofia, Bulgaria, May 2-4, 2008.
[15]
IoanDzitac, IoanaMoisil, Advanced AI Techniques for Web Mining mathematical Methods, Computational Techniques, Non-Linear Systems, Intelligent Systems.
[16]
ZakariaSulimanZubi, Using Some Web Content Mining Techniques for Arabic Text Classification, Recent Advances On Data Networks, Communications, Computers.
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