Abstract
Distributed Data Mining (DDM) has been very active and enjoying a growing amount attention since its inception. Current DDM techniques regard the distributed data sets as a single virtual table and assume there exists a global model which could be generated if the data were combined/centralized. This paper proposes a similarity-based distributed data mining(SBDDM) framework which explicitly take the differences among distributed sources into consideration. A new similarity measure is introduced and its effectiveness is then evaluated and validated. This paper also illustrates the limitations of current DDM techniques through three concrete case studies. Finally distributed clustering within the SBDDM framework is also discussed.