Parallel K-Means Clustering Algorithm on DNA Dataset

  • Fazilah Othman
  • Rosni Abdullah
  • Nur’Aini Abdul Rashid
  • Rosalina Abdul Salam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3320)


Clustering is a division of data into groups of similar objects. K-means has been used in many clustering work because of the ease of the algorithm. Our main effort is to parallelize the k-means clustering algorithm. The parallel version is implemented based on the inherent parallelism during the Distance Calculation and Centroid Update phases. The parallel K-means algorithm is designed in such a way that each P participating node is responsible for handling n/P data points. We run the program on a Linux Cluster with a maximum of eight nodes using message-passing programming model. We examined the performance based on the percentage of correct answers and its speed-up performance. The outcome shows that our parallel K-means program performs relatively well on large datasets.


Master Node Artificial Dataset Inherent Parallelism Positional Weight Matrice Distribute Memory Multiprocessor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Fazilah Othman
    • 1
  • Rosni Abdullah
    • 1
  • Nur’Aini Abdul Rashid
    • 1
  • Rosalina Abdul Salam
    • 1
  1. 1.School of Computer ScienceUniversiti Sains MalaysiaPenangMalaysia

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