Mining changes in association rules: A fuzzy approach

Wai Ho Au, Chun Chung Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

46 Citations (Scopus)


Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Existing algorithms typically assume that data characteristics are stable over time. Their main focus is therefore to mine association rules in an efficient manner. However, the world constantly changes. This makes the characteristics of real-life entities represented by the data and hence the associations hidden in the data change over time. Detecting and adapting to the changes are usually critical to the success of many business organizations. This paper presents the problem of mining changes in association rules. Given a set of database partitions, each of which contains a set of transactions collected in a specific time period, a set of association rules is discovered in each database partition. We propose to perform data mining in the discovered association rules so as to reveal the regularities governing how the rules change in different time periods. Since the nature of many real-life entities is rather fuzzy, we propose to use linguistic variables and linguistic terms to represent the changes in the discovered association rules. In particular, fuzzy decision trees are built to discover the changes in the discovered association rules. The fuzzy decision trees are then converted to a set of fuzzy rules, called fuzzy meta-rules because they are rules about rules. By doing so, the changes hidden in the data can be revealed and presented to human users in a comprehensible form. Furthermore, the discovered changes can also be used to predict any change in the future. To evaluate the performance of our approach, we make use of a set of synthetic datasets, which are database partitions collected in different time periods. A set of association rules is discovered in each dataset. Fuzzy decision trees are constructed in the discovered association rules in order to reveal the changes in these rules. The experimental results show that our approach is very promising.
Original languageEnglish
Pages (from-to)87-104
Number of pages18
JournalFuzzy Sets and Systems
Issue number1
Publication statusPublished - 1 Jan 2005


  • Approximate reasoning
  • Association rules
  • Change mining
  • Data mining
  • Evolving data
  • Fuzzy decision trees
  • Trends

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence


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