Main Reference PaperOptimized big data K-means clustering using MapReduce, The Journal of Supercomputing, 2014 .
  • Processing the large amount of data in hadoop is done using the k-means clustering algorithm. In focus of improving the speed ofclustering of large-scale dataset, this system propose a MapReduce is applied to optimize the big data among the mutinode cluster. TheMapReduce framework use the sampling process to eliminate the iteration dependence of K-means and gets high performance in thehadoop environment.

+ Description
  • Processing the large amount of data in hadoop is done using the k-means clustering algorithm. In focus of improving the speed ofclustering of large-scale dataset, this system propose a MapReduce is applied to optimize the big data among the mutinode cluster. TheMapReduce framework use the sampling process to eliminate the iteration dependence of K-means and gets high performance in thehadoop environment.

  • Reducing computation cost.

  • To achieve scalability.

  • To eliminate the iteration dependency.

  • To process the large volume of data among the multinode cluster.

+ Aim & Objectives
  • Reducing computation cost.

  • To achieve scalability.

  • To eliminate the iteration dependency.

  • To process the large volume of data among the multinode cluster.

  • This work may contributes in cluster formation of the weight based merge clustering and distribution based merge clustering.

+ Contribution
  • This work may contributes in cluster formation of the weight based merge clustering and distribution based merge clustering.

  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2SE, Hadoop.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2SE, Hadoop.

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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