An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2015 by IJCTT Journal | ||
| Volume-30 Number-2 |
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| Year of Publication : 2015 | ||
| Authors : Poorna Banerjee Dasgupta | ||
| DOI : 10.14445/22312803/IJCTT-V30P120 |
Poorna Banerjee Dasgupta "An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets". International Journal of Computer Trends and Technology (IJCTT) V30(2):113-115, December 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
The procedure of Least Square-Errors
curve fitting is extensively used in many computer
applications for fitting a polynomial curve of a given
degree to approximate a set of data. Although
various methodologies exist to carry out curve fitting
on data, most of them have shortcomings with
respect to efficiency especially where huge datasets
are involved. This paper proposes and analyzes a
matricized approach to the Least Square-Errors
curve fitting with the primary objective of
parallelizing the whole algorithm so that high
performance efficiency can be achieved when
algorithmic execution takes place on colossal
datasets.
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Keywords
Data Approximation, Least Square-
Errors, Parallel Computing, High Performance
Computing.