Efficiently mining frequent itemsets in transactional databases

Salah Alghyaline*, Jun-Wei Hsieh, Jim Z.C. Lai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Discovering frequent itemsets is an essential task in association rules mining and it is considered to be computationally expensive. To find the frequent itemsets, the algorithm of frequent pattern growth (FP-growth) is one of the best algorithms for mining frequent patterns. However, many experimental results have shown that building conditional FP-trees during mining data using this FP-growth method will consume most of CPU time. In addition, it requires a lot of space to save the FP-trees. This paper presents a new approach for mining frequent item sets from a transactional database without building the conditional FP-trees. Thus, lots of computing time and memory space can be saved. Experimental results indicate that our method can reduce lots of running time and memory usage based on the datasets obtained from the FIMI repository website.

Original languageEnglish
Pages (from-to)184-191
Number of pages8
JournalJournal of Marine Science and Technology (Taiwan)
Issue number2
StatePublished - 1 Jan 2016


  • Data mining
  • FP-growth
  • Frequent itemsets
  • Frequent pattern


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