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Big Data Clustering using Parallel Differential Evolution Algorithm

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dc.contributor.author Cho, Pyae Pyae Win
dc.contributor.author Nyunt, Thi Thi Soe
dc.date.accessioned 2019-07-03T06:55:12Z
dc.date.available 2019-07-03T06:55:12Z
dc.date.issued 2018-02-22
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/254
dc.description.abstract Clustering is the task of discovering group of similar objects or items and there have been many applications for clustering such as image segmentation, document retrieval and data mining. The increasing volumes of information emerging by the development of technology makes clustering of very large scale of data a challenging task. Differential evolution (DE) algorithm is an innovative evolutionary algorithm (EA) for global optimization, where the mutation operator is based on the distribution of solutions in the population. Clustering can be viewed as optimization problem where the task is finding the optimal cluster solution. To deal with clustering of huge amount of data sets, the use of classical DE is time-consuming that it is infeasible. This paper proposes a parallel differential evolution algorithm for clustering enormous data based on Spark framework. The proposed approach will be efficient for large-scale data clustering. en_US
dc.language.iso en en_US
dc.publisher Sixteenth International Conferences on Computer Applications(ICCA 2018) en_US
dc.title Big Data Clustering using Parallel Differential Evolution Algorithm en_US
dc.type Article en_US

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