Operating System Process Modeling: An Implementation of Association Learning Algorithms using Router Kernel Simulated Data
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2015 by IJCTT Journal | ||
| Volume-24 Number-3 |
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| Year of Publication : 2015 | ||
| Authors : Adamade Peter Simon, Sadiq Mobolaji Abubakar, Anyama Oscar Uzoma | ||
| DOI : 10.14445/22312803/IJCTT-V24P123 |
Adamade Peter Simon, Sadiq Mobolaji Abubakar, Anyama Oscar Uzoma "Operating System Process Modeling: An Implementation of Association Learning Algorithms using Router Kernel Simulated Data". International Journal of Computer Trends and Technology (IJCTT) V24(3):102-107, June 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Large chunk of dispersed data exists in several
databases and data marts, these amount of data if not properly
gathered and analyzed will lead to total loss of useful knowledge.
With the existence of the problem of an efficient scheduling and
resource management techniques in Operating System, there is a dire
need to provide a rule-based scheme to help optimize and maintain
the operating system process modeling in a very efficient manner. To
help improve on this issue, data mining techniques such as data
extraction, cleaning and association rules have been used, Hence,
this paper aims at investigating two of the most efficient learning
association algorithms, FP-Growth and Apriori algorithms with the
objective of helping understand the process of association learning
in a network environment using router kernel data.. This is
implemented using Rapid Miner tool to model the kernel data and
further comparison of the two methods.
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Keywords
FG-Growth, Apriori Algorithm, Machine Learning,
Data mining, Multiprogramming.