TY  - JOUR
T1  - Improving MapReduce Based k-Means Algorithm using Intelligent Technique
AU - Hussien, Hany H. AU - Elssayad, Omar M. AU - El-Zoghabi, Adel A. 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 5
SP  - 150
EP  - 159
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.150.159
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.150.159
KW  - Big data
KW  -clustering
KW  -k-Means algorithm
KW  -MapReduce
KW  -data mining
KW  -feature selection
AB  - Data has expanded exponentially in recent years, leading to a search for ways of how to take
advantage of these data, leading to the rise of big data science. The currently used data analysis techniques are
not successful for different properties such as volume, velocity and variety, so, they are required to invent a new
artificial intelligence technique. k-Means is one of the popular algorithms used to cluster large amounts of data
and the problem with a k-Means is to pick the initial center for each unit which involves an optimization
approach to solve the weak point such a Genetic algorithm which provides optimum performance in search
algorithms that can be used to look for the initial point of each cluster rather than random selection of the initial
centre. Big data involves many techniques, the most popular of which is the MapReduce which used to manage
very large data. The proposed model is a Gk-Means based on the feature selection method parallel to
MapReduce applying a Genetic algorithm to k-Means on selected data.
ER  -