A Study of Big Data Security on a Partitional Clustering Algorithm with Perturbation Technique

Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA di...

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Vydáno v:2020 International Conference on Smart Electronics and Communication (ICOSEC) s. 482 - 486
Hlavní autoři: Marichamy, V. Santhana, Natarajan, V.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2020
Témata:
Accuracy
Big Data
Clustering algorithms
Clustering Tme
Conferences
Execution Time
F-Socre Measure
HDFS
Partitioning algorithms
PCA
Perturbation methods
Precision
Principal component analysis
Privacy Preserving
Security
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Shrnutí:Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA discovers the cluster based on the initial partition of the data. In this approach, it is possible to develop a security safeguarding of data that is impoverished to allow the calculations and communication. The performances were analyzed on Health Care database under the studies of various parameters like precision, accuracy, and F-score measure. The outcome of the results is to demonstrate that this method is used to decrease the complication in preserving privacy and better accuracy than that of the existing techniques.
DOI:10.1109/ICOSEC49089.2020.9215241