Developing a parallel classifier for mining in big data sets

Ahad Shamseen, Morteza Mohammadi Zanjireh, Mahdi Bahaghighat, Qin Xin

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
27 Downloads (Pure)


ABSTRACT: Data mining is the extraction of information and its roles from a vast amount of data. This topic is one of the most important topics these days. Nowadays, massive amounts of data are generated and stored each day. This data has useful information in different fields that attract programmers’ and engineers’ attention. One of the primary data mining classifying algorithms is the decision tree. Decision tree techniques have several advantages but also present drawbacks. One of its main drawbacks is its need to reside its data in the main memory. SPRINT is one of the decision tree builder classifiers that has proposed a fix for this problem. In this paper, our research developed a new parallel decision tree classifier by working on SPRINT results. Our experimental results show considerable improvements in terms of the runtime and memory requirements compared to the SPRINT classifier. Our proposed classifier algorithm could be implemented in serial and parallel environments and can deal with big data.
Original languageEnglish
Pages (from-to)119-134
Number of pages16
JournalIIUM Engineering Journal
Issue number2
Publication statusPublished - 4 Jul 2021


  • Big data
  • Data mining
  • Decision tree
  • Parallel classifier


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