Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout

Authors

  • Sasanka Kasyap B Department of CSE, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, A.P, India
  • K Syama Sundara Rao Department of CSE, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, A.P, India

Keywords:

Big Data, Hadoop, Map Reduce, Mahout, K-Means

Abstract

The Item set Mining is one of the most well known techniques to extract knowledge from data. The mechanism having some problematic data, for those further enhancements have been applied based on the Big Data in which some performances has-been explores on Map Reduce machine. The new approach for mining large datasets such as K-means, Mahout which targets on speed and time while Big FIM is optimized to run on really large datasets. K-means algorithm depends on Map Reduce, which is the infrastructure for prepare more datasets of certain scattered clusters. These clusters are combined in the form of nodes and edges and also display the item sets. The execution of these clusters can also be implemented on large datasets also with high scalability and performance.

References

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Published

2025-11-10

How to Cite

[1]
B. Sasanka Kasyap and K. Syama Sundara Rao, “Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout”, Int. J. Comp. Sci. Eng., vol. 3, no. 10, pp. 43–46, Nov. 2025.

Issue

Vol. 3 No. 10 (2015): IJCSE October Edition

Section

Research Article

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