Mining Closed Colossal Frequent Patterns from High-Dimensional Dataset: Serial Versus Parallel Framework

  • Sudeep SureshanEmail author
  • Anusha Penumacha
  • Siddharth Jain
  • Manjunath Vanahalli
  • Nagamma Patil
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)


Mining colossal patterns is one of the budding fields with a lot of applications, especially in the field of bioinformatics and genetics. Gene sequences contain inherent information. Mining colossal patterns in such sequences can further help in their study and improve prediction accuracy. The increase in average transaction length reduces the efficiency and effectiveness of existing closed frequent pattern mining algorithm. The traditional algorithms expend most of the running time in mining huge amount of minute and midsize patterns which do not enclose valuable information. The recent research focused on mining large cardinality patterns called as colossal patterns which possess valuable information. A novel parallel algorithm has been proposed to extract the closed colossal frequent patterns from high-dimensional datasets. The algorithm has been implemented on Hadoop framework to exploit its inherent distributed parallelism using MapReduce programming model. The experiment results highlight that the proposed parallel algorithm on Hadoop framework gives an efficient performance in terms of execution time compared to the existing algorithms.


Frequent patterns Closed patterns Minimum support Closed colossal frequent patterns High-dimensional datasets Hadoop MapReduce 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sudeep Sureshan
    • 1
    Email author
  • Anusha Penumacha
    • 1
  • Siddharth Jain
    • 1
  • Manjunath Vanahalli
    • 1
  • Nagamma Patil
    • 1
  1. 1.National Institute of Technology KarnatakaSurathkal, MangaloreIndia

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