Survey on Data Mining and Ontology for handling Big Data

Volume: 1    Issue: 3

Year of Publication: 2015

Pages: (23-26)

Authors: Mr. N. Suresh Kumar, Dr. M. Thangamani

Download Full Text (PDF)

Abstract

Big data is large volume, heterogeneous, distributed data and now rapidly expanding in all science and engineering domains. Big Data mining is the ability of extracting useful information from large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. With increasing size of data in data warehouse it is expensive to perform data analysis. This survey includes the information about handling big data with data mining Data.

References

1. shridharan .B, Tretiakov .A and Kinshuk (2004), “Application of ontology to knowledge management in web based learning”, In IEEE International Conference on Advanced Learning Technologies, pp. 663 – 665.

2. Visser .P.R.S and Tamma .V.A.M (1999), “An Experience with Ontology-based Agent Clustering”, In Proceedings of the IJCAI\"99 Workshop on Ontology and Problem-Solving Methods: Lesson learned and Future Trends, Benjamins .V.R (Ed.), CEUR Publications, Amsterdam, Vol. 18, pp. 12.1-12.13.

3. Guarino .N (1998), “Formal Ontology and Information Systems”, In Proceedings of Conference on Formal Ontology (FOIS\"98), Guarino .N (Ed.), Trento.

4. Gomez Perez .A and Benjamins .V.R (1999), “Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem-Solving Methods”, In Proceedings of the IJCAI\"99 Workshop on Ontology and Problem-Solving Methods: Lesson learned and Future Trends, Benjamins, V.R., (Ed.), CEUR Publications, Amsterdam, Vol. 18, pp. 1.1-1.15.

5. Jia Zhen and Wang Yong Gui (2010), “Genetic Clustering Algorithm Based on Dynamic Granularity”, International Conference on Computing, Control and Industrial Engineering (CCIE), Vol. 1, pp. 431-434.

6. Zhongjun Deng, Wei Song and Xuefeng Zheng (2010), “P2PKMM: A Hybrid Clustering Algorithm over P2P Network”, Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI), pp. 450–454.

7. Qing He, Tingting Li, Fuzhen Zhuang and Zhongzhi Shi (2010), “Frequent term based peer-to-peer text clustering”, 3rd International Symposium on Knowledge Acquisition and Modeling (KAM), pp. 352–355.

8. S. H. Lee, J. G. Lee and K. I. Moon, “A preprocessing of Rough Sets Based on Attribute Variation Minimization” , International Journal of Software Engineering and Its Applications, vol. 7, no. 6,(2013), pp. 411-424.

9. Dhillon .I.S and Modha .D.S (2001), “Concept decompositions for large sparse text data using clustering”, Machine Learning, Vol. 42, pp. 143-175.

10. S. Mal and K. Rajnish, New Quality Inheritance Metrics for Object-Oriented Design”, International Journal of Software Engineering and Its Applications, vol. 7, no. 6, (2013) , pp.185-200.

11. A. Ngoumou and M. F. Ndjodo, “A Rewriting System Based Operational Semantics for the feature Oriented Resue Method” , International Journal of Software Engineering and Its Applications, vol. 7, no. 6, (2013), pp. 41-60.

12. Mu-Hsing Kuo; Tony Sahama; Andre W. Kushniruk; Elizabeth M. Borycki; Daniel K. Grunwell, Health big data analytics: current perspectives, challenges and potential solutions, International journal of big data intelligence, interscience, Vol. 4, issue 8, pp.114 – 126.

13. Amy J.C Trappey, Charles V. Trappey, Fu-Chiang Hsu, and David W. Hsiao (2009), “A Fuzzy Ontological Knowledge Document Clustering Methodology”, IEEE Transactions on System, Man and Cybernetics-Part B, Vol. 39, No. 3.

14. Herland, M. ; Florida Atlantic Univ., Boca Raton, FL, USA ; Khoshgoftaar, T.M. ; Wald, R., Survey of Clinical Data Mining Applications on Big Data in Health Informatics, IEEE, Machine Learning and Applications (ICMLA), 2013 12th International Conference , vol 2 pp. 465 – 472.

15. Shital Suryawanshi1, Prof. V.S.Wadne2 , Big Data Mining using Map Reduce: A Survey Paper , IOSR Journal of Computer Engineering, Volume 16, Issue 6, Ver. VII (Nov – Dec. 2014), PP 37-40.

16. Cross .V (2004), “Fuzzy Semantic Distance Measures between Ontological Concepts”, IEEE Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004), Banff, Alberta, Canada, pp. 27–30.

17. Thomas .C and Sheth .A (2006), “On the Expressiveness of the Languages for the Semantic Web – Making a Case for ‘A Little More”, Fuzzy Logic and the Semantic Web (Capturing Intelligence), Sanchez .E (Ed.), Elsevier.

18. Jiawei Han, MichelineKamber,”Data Mining Concepts and Techniques”, Second Edition, Morgan Kaufman Publishers, 2006 Elsevier In.

19. Dr. M. Thangamani, A. Senthil karthick kumar, A. M. J. Mohamed zubair Rahman, Bio-Inspired Fuzzy Expert system for Mining Big data, Mathematical and Computational Methods in Science and Engineering, pp. 223-227, 2014

20. Yong-Il Kim1, Yoo-Kang Ji2 andSun Park3, Big Text Data Clustering using Class Labels and Semantic Feature Based on Hadoop of Cloud Computing, International Journal of Software Engineering and Its Applications, Vol.8, No. 4 (2014), pp. 1-10.

21. Tom White, “Hadoop: The Definitive Guide”, First Edition, Published by Reilly Media, June 2009, in United States of America.ao,Tao Li , IEEE transaction,2012.

22. Konstantin Shvachko, HairongKuang, “The Hadoop Distributed File System”, Published by IEEE, 2010.

23. Somesh S Chavadi1, Dr. Asha T2, Text Mining Approach for Big Data Analysis Using Clustering and Classification Methodologies, International Journal of Emerging Technology and Advanced Engineering, Vol. 4, issue 8, 2014.

24. Yi Wu, Network Big Data: A Literature Survey on Stream Data Mining, Journal of Software, vol. 9, no. 9, 2014, pp-2427-2434

Keywords

Data Mining, Big data and Clustering.