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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
MEFASD-BD: Multi-objective evolutionary fuzzy algorithm for subgroup discovery in big data environments - A MapReduce solution
ترجمه فارسی عنوان مقاله:
MEFASD-BD: الگوریتم فازی تکاملی چند هدفه برای کشف زیرگروه در محیط های داده های بزرگ - یک راه حل MapReduce
منبع:
Sciencedirect - Elsevier - Knowledge-Based Systems 117 (2017) 70–78
نویسنده:
F. Pulgar-Rubio a, A.J. Rivera-Rivas a, M.D. Pérez-Godoy a, P. González a, C.J. Carmona b M.J. del Jesus a
چکیده انگلیسی:
Nowadays, there is an incredible increase of data volumes around the world, with the Internet as one of
the main actors in this scenario and a growth rate above 30GB/s. The treatment of this huge amount of
information cannot be carried out through traditional data mining algorithms in an efficient way and it
is necessary to adapt and design new algorithms towards distributed paradigms such as MapReduce. This
situation is a challenge for the community, investigated under the widely known term of big data.
This paper presents a new algorithm for the subgroup discovery task called MEFASD-BD. The algo
rithm is developed in Apache Spark based on the MapReduce paradigm, and it is able to tackle high di
mensional datasets in an efficient way. In fact, this algorithm is the first approximation to big data within
evolutionary fuzzy systems for subgroup discovery. MEFASD-BD implements novel MapReduce functions
which are able to analyse the quality of the subgroups obtained for each map with respect to the orig
inal dataset in order to improve the quality of these subgroups. In addition, the final reduce function of
the algorithm employs the token competition operator in order to select the best rules extracted in the
different maps. An experimental study with high dimensional datasets is performed in order to show the
advantages of this algorithm in this type of problems. Specifically, the results of the study show an im
portant reduction of the runtime while keeping the values in the standard quality measures for subgroup
discovery.
Keywords:Subgroup discovery|Big data|Multi-objective evolutionary fuzzy systems|Apache Spark|MapReduce
قیمت: رایگان
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