Unsupervised KDD to creatively support managers' decision making with fuzzy association rules: a distribution channel application

Albert Orriols-Puig, Francisco J. Martínez-López*, Jorge Casillas, Nick Lee

*Corresponding author for this work

Research output: Contribution to journalSpecial issuepeer-review


To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis.

Original languageEnglish
Pages (from-to)532-543
Number of pages12
JournalIndustrial Marketing Management
Issue number4
Early online date26 Mar 2013
Publication statusPublished - May 2013


  • genetic fuzzy systems
  • intelligent systems
  • KDD
  • management support
  • unsupervised learning


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