An Efficient MapReduce-Based Apriori-Like Algorithm for Mining Frequent Itemsets from Big Data

  • Ching-Ming Chao
  • Po-Zung Chen
  • Shih-Yang YangEmail author
  • Cheng-Hung Yen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 264)


Data mining can discover valuable information from large amounts of data so as to utilize this information to enhance personal or organizational competitiveness. Apriori is a classic algorithm for mining frequent itemsets. Recently, with rapid growth of the Internet as well as fast development of information and communications technology, the amount of data is augmented in an explosive fashion at a speed of tens of petabytes per day. These rapidly expensive data are characterized by huge amount, high speed, continuous arrival, real-time, and unpredictability. Traditional data mining algorithms are not applicable. Therefore, big data mining has become an important research issue.

Clouding computing is a key technique for big data. In this paper, we study the issue of applying cloud computing to mining frequent itemsets from big data. We propose a MapReduce-based Apriori-like frequent itemset mining algorithm called Apriori-MapReduce (abbreviated as AMR). The salient feature of AMR is that it deletes the items of itemsets lower than the minimum support from the transaction database. In such a way, it can greatly reduce the generation of candidate itemsets to avoid a memory shortage and an overload to I/O and CPU, so that a better mining efficiency can be achieved. Empirical studies show that the processing efficiency of the AMR algorithm is superior to that of another efficient MapReduce-based Apriori algorithm under various minimum supports and numbers of transactions.


Data mining Frequent itemsets Big data MapReduce Apriori 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Ching-Ming Chao
    • 1
  • Po-Zung Chen
    • 2
  • Shih-Yang Yang
    • 3
    Email author
  • Cheng-Hung Yen
    • 1
  1. 1.Department of Computer Science and Information ManagementSoochow UniversityTaipeiTaiwan
  2. 2.Department of Computer Science and Information EngineeringTamkang UniversityTaipeiTaiwan
  3. 3.Department of Media Art and Management of Information SystemUniversity of Kang NingTaipeiTaiwan

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