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

fYear

2008

fDate

6-10 Oct. 2008

Firstpage

126

Lastpage

131

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science, 2008. ENC '08. Mexican International Conference on

Conference_Location

Baja California

ISSN

1550-4069

Print_ISBN

978-0-7695-3439-8

Type

conf

DOI

10.1109/ENC.2008.17

Filename

4653247