| Distributed data mining (DDM) is the semi-automatic pattern extraction of distributed data sources. DDM requires two main decisions about the DDM implementations: A distributed computation paradigm (message passing, RPC, mobile agents), and the used integration techniques (Knowledge probing, CDM) in order to aggregate and integrate the results of the various distributed data miners. Recently, the new distributed computation paradigm that has been evolved as mobile agent is widely used.; The thesis proposes a new model that can benefit from the mobile agent paradigm to build an efficient DDM model. Since the size of the data to be migrated in the DDM process is huge, our model will overcome the communication bottleneck by using mobile agents paradigm. Our model divides the DDM process into several stages that can be done in parallel on different data sources: Preparation stage, data mining stage and knowledge integration stage.; The derivation of the cost functions for various DDM models including the proposed model is also presented and analyzed. (Abstract shortened by UMI.)... |
