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Frontiers of Supercomputing
Vol. 3, No. 1, Apr. 2020
High Performance Clustering to Improve Data Mining Techniques | Miscellaneous
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a University of Baghdad, Baghdad, Iraq
Prof. Heba Fadhil
 Corresponding Author
 Affiliation: University of Baghdad, Baghdad, Iraq
 Tel: 773
Phone: 2026138  E-mail: fadhilheba@gmail.com
2nd e-mail: heba@kecbu.uobaghdad.edu.iq
Prof. Heba Fadhil's publications in ICSES
- Task Scheduling in the Cloud Amended Through Genetic Algorithms
ICSES Transactions on Evolutionary and Metaheuristic Algorithms
Miscellaneous | Vol. 6, No. 1 | Pages 1-2 | Apr. 2020
Heba Fadhil - High Performance Clustering to Improve Data Mining Techniques
Frontiers of Supercomputing
Miscellaneous | Vol. 3, No. 1 | Pages 1-2 | Apr. 2020
Heba Fadhil - Parallel Programming Models for Cloud Computing
ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data
Special Issue Proposal | In Press | Pages 1-4 | Mar. 2020
Heba Fadhil - Private Cloud Data Storage Using Raspberry Pi
ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data
Short Letter | Vol. 3, No. 3 | Pages 1-5 | Sep. 2019
Heba Fadhil - The Age of Miraculous Technology Advent to Eradicate Cancer Cells
ICSES Transactions on Image Processing and Pattern Recognition
Miscellaneous | Vol. 4, No. 2 | Pages 1-3 | Jun. 2018
Heba Fadhil - Cloud Precept: Storage, Backup, and Synchronization
ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data
Short Letter | Vol. 2, No. 1 | Pages 1-3 | Mar. 2018
Heba Fadhil
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This article has been retracted by International Computer Science and Engineering Society (ICSES) because of ethical misconduct, scientific distortion, or administrative error, and cannot be downloaded and used for any purpose based on the violation in ICSES Ethics in Publicationcall_made |
Retraction Note by the Editor-in-Chief
Highlights and Novelties
1- An increasing amount of data is approaching into existence each day and power to process this data calls for cluster computers which are a set of a computer that operates together, only to be viewed as a single computer, to process and compute data.
2- People are pretty dependent on computers and their computational power more and more every day. As a consequence, those outcomes need to be accurate and be calculated fast.
3- Parallel computing is a more rapid way to process instructions by breaking the large task into smaller tasks using an organized effort to process data simultaneously.
4- Data clustering has been confirmed to be a favorable data mining technique. Recently, there have been various attempts for clustering data mining algorithms.
Manuscript Abstract
An increasing amount of data is approaching into existence each day and power to process this data calls for cluster computers which are a set of a computer that operates together, only to be viewed as a single computer, to process and compute data. People are pretty dependent on computers and their computational power more and more every day. As a consequence, those outcomes need to be accurate and be calculated fast. Parallel computing is a more rapid way to process instructions by breaking the large task into smaller tasks using an organized effort to process data simultaneously. Data clustering has been confirmed to be a favorable data mining technique. Recently, there have been various attempts for clustering data mining algorithms. For that reason, Cluster computing is used to create redundancy in a computer network to ensure that it will regularly be to be had and that it is going to not fail.
Keywords
Data clustering Data mining Parallel programming High performance
Copyright and Licence
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors. This manuscript is published in Open-Access manner based on the copyright licence of Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0).
Cite this manuscript as
Heba Fadhil, "High Performance Clustering to Improve Data Mining Techniques," Frontiers of Supercomputing, vol. 3, no. 1, pp. 1-2, Apr. 2020.
For External Scientific Databeses
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