Published 2023-12-31
Keywords
- Big Data Mining, Cloud Computing Technique, Clustering Techniques, Collaborative filtering, Data Mining and Data Slicing.
How to Cite
Abstract
Big data processing presents itself as a novel and promising analytical field for extracting useful information from enormous databases. It is used to handle vast volumes of knowledge sets, usually large, sparse, incomplete, uncertain, complex, or dynamic information set from various and autonomous sources, in time-sensitive applications such as social site data processing and medical applications. In order for the user to easily obtain the main strategy and answers to their questions from the mined results, massive data processing also handles the storage structure of the mined results. Information slicing is done to break up the associations between columns while keeping the associations within each column. There are several types of information slicing: quasi-static, amorphous, simultaneous dynamic, quasi-static, and dynamic. Another fundamental duty in the huge information mining process is clustering, which is used to find patterns and identify information for use in large-scale processing applications. In addition to discussing the benefits and limitations of these strategies, this study examines huge data processing, information slicing, and clustering techniques. Information slicing and clumping approaches, mining platforms, and large data mining algorithms are discussed along with their quality and performance.
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References
[2] H. Wang, Y. Shen, L. Wang, K. Zhufeng, W. Wang, and C. Cheng, "Large-scale multimedia data mining using MapReduce framework," in CloudCom, 2012, pp.287-292.
[3] H. Aksu, M. Canim, Y.-C. Chang, I. Korpeoglu, and O. Ulusoy, "Multi-resolution Social Network Community Identification and Maintenance on Big Data Platform," in IEEE International Congress on Big Data (BigData Congress), pp. 102-109,2013.
[4] T. Rabl, S. Gómez-Villamor, M. Sadoghi, V. Muntés-Mulero, H.-A. Jacobsen, and S. Mankovskii, "Solving big data challenges for enterprise application performance management," Proceedings of the VLDB Endowment, vol. 5, pp. 1724-1735,2012.
[5] Dr. P.Logeswari “Extraction of Subset- Want in Data Stream using EMDMICA Algorithm “ Volume 7 Issue VI, June 2019.
[6] Dr. P.Logeswari, J.Gokulapriya “A Literature Review on Data Mining Techniques “in July Volume -7 Issue -7.
[7] .Dr. P.Logeswari, J.Gokulapriya “Literature Survey on Big Data mining And Its Algorithmic Techniques “in July Volume -8 Issue7.
[8] Dr. P.Logeswari, G.Banupriya “A Survey on Implementations Solutions for Attack Prevention Cryptography Technique’s in WSN UsingNS2” Volume 7,Issue 6 June 2021.
[9] Dr. P.Logeswari, G.Banupriya “Review on Cryptography Techniques in WSN for Attack Prevention” volume 8, Issue 8.
[10] Dr. P.Logeswari, S.Sudha “A Survey on Privacy Preserving in Data Mining”Volume-7, Issue-8 August 2021.
[11] Dr. P.Logeswari, S.Sudha “A Review on Privacy Preserving in Data Mining” Volume-8, Issue-6 June2021.
[12] Sangeetha, J. and Prakash, V.S., 2017. A survey on big data mining techniques. International Journal of Computer Science and Information Security, 15(1), p.482.
[13] Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., Zheng, S., Xu, A. and Lyu, J., 2020. Brief introduction of medical database and data mining technology in big data era. Journal of Evidence‐Based Medicine, 13(1), pp.57-69.
[14] Hussan, M.I.T., Reddy, G.V., Anitha, P.T. et al. DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization. Cluster Comput (2023).
[15] Nti, I.K., Quarcoo, J.A., Aning, J. and Fosu, G.K., 2022. A mini-review of machine learning in big data analytics: Applications, challenges, and prospects. Big Data Mining and Analytics, 5(2), pp.81-97.
[16] George, A.H., Shahul, A., George, A.S., Baskar, T. and Hameed, A.S., 2023. A Survey Study on Big Data Analytics to Predict Diabetes Diseases Using Supervised Classification Methods. Partners Universal International Innovation Journal, 1(1), pp.1-8.
[17] Joseph Gladju, Ayyasamy Kanagaraj, Kamalam Biju Sam, Use of data mining to establish associations between Indian marine fish catch and environmental data, Archives of Biological Sciences, Vol. 75 No. 4 (2023),pp. 459-474.
[18] J Gladju, BS Kamalam, A Kanagaraj,(2022), Applications of data mining and machine learning framework in aquaculture and fisheries: A review, Smart Agricultural Technology,vol. 2. pp.1-15.