SAS: Implementation of scaled association rules on spatial multidimensional quantitative dataset

SAS: Implementation of scaled association rules on spatial multidimensional quantitative dataset

Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a sh...

Spatial Multidimensional Association Rules Mining in Forest Fire Data

Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau ...

Mining Quantitative Association Rules on Overlapped Intervals

Further remarks on Quantitative Association Rules Mining

Efficient algorithms for discovering association rules from binary data already exist. However, most of real world databases are not boolean. The problem of expanding these algorithms to handle variety types of data such as quantitative data has been attracted the attention of many researchers. In this paper we provide a comparison of existing algorithms for generating association rules from qu...

Multidimensional Association Rules in Boolean Tensors

Popular data mining methods support knowledge discovery from patterns that hold in binary relations. We study the generalization of association rule mining within arbitrary n-ary relations and thus Boolean tensors instead of Boolean matrices. Indeed, many datasets of interest correspond to relations whose number of dimensions is greater or equal to 3. However, just a few proposals deal with rul...