Inferring implicit rules by learning explicit and hidden item dependency

Shoujin Wang, Longbing Cao

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.
Original languageEnglish
Pages (from-to)935-946
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number3
Early online date13 Dec 2017
Publication statusPublished - Mar 2020
Externally publishedYes


  • Correlation
  • Data mining
  • Decision making
  • Hidden dependency
  • implicit rules
  • Itemsets
  • Optimization
  • pattern relation analysis
  • rule inference
  • Sprites (computer)


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