A Novel Algorithm for Frequent Itemsets Mining in Transactional Databases

  • Huan PhanEmail author
  • Bac Le
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)


Since the era of data explosion, data mining in transactional databases has become more and more important. There are many data mining techniques like association rule mining, the most important and well-researched one. Furthermore, frequent itemset mining is one of the fundamental but time-consuming steps in association rule mining. Most of the algorithms used in literature find frequent itemsets on search space items having at least a minsup and are not reused for mining next time. To deal with this problem, NOV-FI algorithms are proposed as a new approach in order to quickly detect frequent itemsets from transactional databases using an array of co-occurrences and occurrences of kernel item in at least one transaction. NOV-FI algorithms are easily expanded in distributed systems. Finally, the experimental results show that the proposed algorithms perform better than other existing algorithms.


Association rules Co-occurrence items Frequent itemsets 



This work was supported by University of Social Sciences and Humanities; University of Science, VNU-HCM, Vietnam.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Division of ITUniversity of Social Sciences and Humanities, VNU-HCMHo Chi Minh CityVietnam
  2. 2.Faculty of Mathematics and Computer ScienceUniversity of Science, VNU-HCMHo Chi Minh CityVietnam
  3. 3.Faculty of ITUniversity of Science, VNU-HCMHo Chi Minh CityVietnam

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