@article{MAKHILLJEAS201813215463,
    title = {Parallel Self-Organizing Map using MapReduce in GPUs Environment},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {2},
    pages = {314-320},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.314.320},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.314.320},
    author = {Faeez,Noor,Muhammad and},
    keywords = {Self-organizing map,enhanced mapreduce,incremental reduction,graphical processing units,enhancement,method},
    abstract = {One of the drawbacks of MapReduce characteristic is overlap communication. It causes
implementation inefficiency in the GPUs environment. However, this can be overcome using incremental
reduction method. This method will enhance the communication process on GPUs environment as an alternative
to execution using CPU. This enhancement is based on Python with support of CUDA technologies which can
execute this whole process in GPUs environment. In order to achieve the good performance, this study is
proposing to design the MapReduce with incremental reduction and then to construct it and finally to test the
enhancement method to the self-organizing map with handwriting dataset.}
    }