Design and Implementation of WSRF-Compliant Grid Services for Mining Fuzzy Association Rules

Data mining is a widely used approach for the transformation of large amounts of data to useful patterns and knowledge. Fuzzy association rules mining is a data mining technique which tries to and association rules without the e ect of sharp boundary problems when data contains continuous and categorical attributes. Grid data mining is a new concept, which allows the data mining process to be deployed and used in a data grid environment where data and service resources are geographically distributed. In this paper, a grid service for mining fuzzy association rules is developed. The service is implemented based on recently proposed Data Mining Grid Architecture (DMGA) and uses the Web Service Resource Framework (WSRF). Experimental evaluations, after implementing and deploying the service, show the e ectiveness and acceptable performance of the proposed grid service. Additionally, in this study, a new algorithm, namely FFDM, is developed to mine fuzzy association rules without raw data exchange, using the distributed storage of data grid environments. Empirical evaluation of FFDM reveals the scalability and e ciency of the proposed method, in addition to the advantages of minimum messaging and providing privacy of data.