scholarly journals P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction

Author(s):  
Frederic T. Stahl ◽  
Max A. Bramer ◽  
Mo Adda
2012 ◽  
Vol 28 (4) ◽  
pp. 451-478 ◽  
Author(s):  
Frederic Stahl ◽  
Max Bramer

AbstractThe fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.


Author(s):  
Vandita Patel

AbstractWe describe a computationally efficient approach to resolving equations of the form $$C_1x^2 + C_2 = y^n$$ C 1 x 2 + C 2 = y n in coprime integers, for fixed values of $$C_1$$ C 1 , $$C_2$$ C 2 subject to further conditions. We make use of a factorisation argument and the Primitive Divisor Theorem due to Bilu, Hanrot and Voutier.


2015 ◽  
Vol 300 ◽  
pp. 779-799 ◽  
Author(s):  
Bernard Parent ◽  
Sergey O. Macheret ◽  
Mikhail N. Shneider

Sadhana ◽  
2014 ◽  
Vol 39 (2) ◽  
pp. 317-331 ◽  
Author(s):  
VILAS H GAIDHANE ◽  
YOGESH V HOTE ◽  
VIJANDER SINGH

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