[HTML][HTML] A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

J Zhang, JS Wong, T Li, Y Pan - International Journal of Approximate …, 2014 - Elsevier
Nowadays, with the volume of data growing at an unprecedented rate, large-scale data
mining and knowledge discovery have become a new challenge. Rough set theory for …

[引用][C] A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

J ZHANG, JS WONG, T LI… - International journal of …, 2014 - pascal-francis.inist.fr
A comparison of parallel large-scale knowledge acquisition using rough set theory on
different MapReduce runtime systems CNRS Inist Pascal-Francis CNRS Pascal and Francis …

A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

J Zhang, JS Wong, T Li, Y Pan - International Journal of Approximate …, 2014 - dl.acm.org
Nowadays, with the volume of data growing at an unprecedented rate, large-scale data
mining and knowledge discovery have become a new challenge. Rough set theory for …

A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

J Zhang, JS Wong, T Li, Y Pan - International Journal of Approximate …, 2014 - infona.pl
Nowadays, with the volume of data growing at an unprecedented rate, large-scale data
mining and knowledge discovery have become a new challenge. Rough set theory for …

A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

J Zhang, JS Wong, T Li, Y Pan - International Journal of Approximate …, 2014 - infona.pl
Nowadays, with the volume of data growing at an unprecedented rate, large-scale data
mining and knowledge discovery have become a new challenge. Rough set theory for …

[引用][C] A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

J ZHANG, JS WONG, T LI, YI PAN - International journal of approximate …, 2014 - Elsevier