Title :
Framework for Discovering Association Rules in a Fuzzy Data Cube
Author :
Somodevilla, Maria J. ; Torres, Ivo H Pineda ; Zecua, José Tecuapacho
Author_Institution :
Fac. de Cienc. de la Comput., Benemerita Univ. Autonoma de Puebla, Puebla
Abstract :
This work presents a framework for the extraction of association rules from a spatial fuzzy data cube. First of all, spatial queries are executed to filter the information to be loaded in the data warehouse considering the spatial relationships among data. Later on, using the Mondrian tool a classic data cube is created and a fuzzy data cube (FDC) is generated from the first cube; by selecting the linguistic variable, the fuzzy sets are defined and a threshold that allow us to determine whether the transactionpsilas value belong to a fuzzy set or other. Once the FDC has been obtained, and also the information be ready to be mined, we use Weka tool in order to extract the association rules from the data. Finally, we apply the proposed framework through an case study: ldquoAnalysis of risk zones of the Popocatepetl Volcanordquo.
Keywords :
data mining; data warehouses; fuzzy set theory; information filtering; query processing; visual databases; Mondrian tool; Weka tool; association rules discovery; data warehouse; fuzzy sets; information filtering; spatial data mining; spatial fuzzy data cube; spatial queries; spatial relationships; Association rules; Computer science; Data analysis; Data mining; Fuzzy logic; Fuzzy sets; Humans; Information filtering; Information filters; Spatial databases; Data; FDC; Fuzzy Sets; Spatial Data Mining;
Conference_Titel :
Computer Science, 2008. ENC '08. Mexican International Conference on
Conference_Location :
Baja California
Print_ISBN :
978-0-7695-3439-8
DOI :
10.1109/ENC.2008.17
