Distributed data mining extracts the knowledge from distributed data sources without considering their physical location. The need for such systems arises from the fact that, in real time many data bases are distributed geographically in different locations.Often transferring data produced at local sites to centralized site for extracting knowledge results in excessive time and transmission cost and may also raise privacy issues. These reasons emphasis on need of distributed mining algorithm. In order to overcome lack of efficient associative classification techniques in field of distributed data mining this paper proposes an associative classification model on distributed databases. By considering the efficiency of fuzzy association rules in providing accuracy, intuitiveness and in overcoming the problem of crisp partitioning this model adopts fuzzy associative rules for classification. The proposed model accuracy tested on UCI data bases given encouraging results

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Associative classification Fuzzy association rules Distributed data bases