Fuzzy association rule mining from spatio-temporal data

Calargun S. U. , YAZICI A.

International Conference on Computational Science and Its Applications (ICCSA 2008), Perugia, Italy, 30 June - 03 July 2008, vol.5072, pp.631-646 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 5072
  • Doi Number: 10.1007/978-3-540-69839-5_47
  • City: Perugia
  • Country: Italy
  • Page Numbers: pp.631-646
  • Keywords: data mining, fuzzy association rules, fuzzy spatio-temporal data cube, association rule mining, association rule mining comparison criteria


The use of fuzzy sets in mining association rules from spatio-temporal databases is useful since fuzzy sets are able to model the uncertainty embedded in the meaning of data. There are several fuzzy association rule mining techniques that can work on spatio-temporal data. Their ability to mine fuzzy association rules has to be compared on a realistic scenario. Besides the performance criteria, other criteria that can express the quality of an association rule discovered shall be specified. In this paper, fuzzy association rule mining is performed with spatio-temporal data cubes and Apriori algorithm. A real life application is developed to compare data cubes and Apriori algorithm according to the following criteria: interpretability, precision, utility, novelty, direct-to-the-point, performance and visualization, which are defined within the scope of this paper.