K-MEAN++ Applied To Solve Problems of Data Security in Data Science
Keywords:
K-MEAN++, Data science, Security Data Encryption Standard algorithmAbstract
In this paper, we describe an application K-MEAN++ clustering algorithm and Data Encryption Standard algorithm for security of information and large volumes data. Data are highly complex multidimensional signals, with rich and complicated information content Data science. For this reason they are difficult to analyze through a unique automated approach. However a K-MEAN++ scheme & Data Encryption Standard are helpful for the understanding of security of data content in Data science. In any system that captures, stores, analyzes, manages, and presents data that are linked to location and like Image satellite sensors acquire huge volumes of imagery to be processed and stored in big archives. Technically, a data science is a data modelling that includes mapping software and its application to data set , land surveying, aerial photography, mathematics, geography, and tools that can be implemented with Data science software Building a hierarchy is a fruitful area if one likes the challenge of having difficult technical problems to solve. Some problems have been solved in other technologies such as database management. However, Data science throws up new demands, therefore requiring new solutions. In this paper we have examine difficult problems, and to be solved and gives some security methods to solve the problem of data security using clustering algorithm.
References
[1] Hao, X, An, H, Zhang, L, Li, H and Wei, G. 2015. Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network. Plos One, 10(10): e0140027. DOI: https://doi.org/10.1371/journal.pone.0140027.
[2] M.C. Burl, C. Fowlkes, and J. Roden, “Mining for image content,” in Systemic, Cybernetics, and Informatics / Information Systems: Analysis and Synthesis, Orlando, FL, July 1999.
[3] L.-K. Soh and C. Tsatsoulis, “Data mining in remotely sensed images: a general model and an application,” in Proceedings of IEEE
[4] IGARSS 1998, vol. 2, Seattle, Washington, USA, Jul 2012, pp. 798-800.
[5] J. Zhang, H. Wynne, M. L. Lee, “Image mining: issues, frameworks, and techniques,” in Proceedings of 2nd International Workshop on Multimedia Data Mining, San Francisco, USA, Aug 2001, pp.13 – 20.
[6] G.B.Marchisio andJ.Cornelison,“Content-based search and clustering of remote sensing imagery,” in Proceedings of IEEE IGARSS 1999, vol. 1, Hamburg, Germany, Jun 1999, pp. 290 – 292.
[7] A.Vellaikal, C.-C.Kuo, and S. Dao, “Content-based retrieval of remote sensed images uses vector quantization,” in Proc. of SPIE Visual Info. Processing IV, vol. 2488, Orlando, USA, Apr 1995, pp.178 – 189.
[8] Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma.A Survey of content-based image retrieval with high- Level Semantics. Pattern Recognition, Volume 40, Issue 1, January 2007, Pages 262-282.
[9] Muhammad Atif Tahir, Ahmed Bouridane, Fatih Kurugollu. Imultaneous feature selection and feature weighting Using Hybrid Tabu Search/K-nearest neighbor classifier. Pattern Recognition Letters, Volume 28, Issue 4, 1 March 2007.
[10] Sarbast Rasheed, Daniel Stashuk, Mohamed Kamel.Adaptive Fuzzy k-NN classifier for EMG signals Decomposition. Medical Engineering & Physics, Volume 28, Issue 7, September 2006, Pages 694-709.
[11] J. Amores, N. SEbE, P. Radeva.Boosting the distance Estimation: Application to the K-Nearest Neighbor Classifier. Patter Recognition Letters, Volume 27, Issue 3, February 2006, Pages 201-209.
[12] Man Wang, Zheng-Lin Ye, Yue Wang, Shu-Xun Wang. Dominant sets clustering for image retrieval. M. Wang et al. /Signal Processing 88 (2008) 2843–2849., Venables W. N. and Ripley B. D. (2000), S Programming, Springer, New York..
[13] Edwards,D.,2005, Excavations at Khirbet Cane, Israel, http://anticompetitive/cane.
[14] M.C. Burl, C. Fowlkes, and J. Roden, “Mining for image content,” in Systemic, Cybernetics, and Informatics / Information Systems: Analysis and Synthesis, Orlando, FL, July 1999.
[15] Zlatanova S.: Large-scale data integration An Introduction to the Challenges for CAD and GIS Integration, Directions magazine, July 10, 2014.
[16] Van Ostracism P.: Bridging the Worlds of CAD and GIS, Directions magazine, June 17, 2004.
[17] David Arthur and Sergei Vassilvitskii: k-means++: The Advantages of
Careful seeding, Proceedings of the eighteenth Annual ACM-SIAM
Symposium on discrete algorithms. pp. 1027—1035, 2007.
[18] Zhang Y, Mao J. and Xiong Z.: An efficient Clustering Algorithm, In
Proceedings of Second International Conference On Machine Learning
And Cyber netics, November 2003.
[19] IEEE Trans. on Knowledge and Data Engineering, 14, No.5, Sept/Oct
2009.
[20] M. E. Hellman, "DES will be totally insecure within ten years", IEEE
Spectrum, vol. 16, no. 7, pp. 32-39, July 1979.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
