Distribution of Data and Computation Allows For Solvinglarger Problems and Execute Applications That Are Distributed In Nature. Thegrid Is a Distributed Computing Infrastructure That Enables Coordinatedresource Sharing Within Dynamic Organizations Consisting of Individuals,Institutions, and Resources. the Grid Extends the Distributed and Parallelcomputing Paradigms Allowing Resource Negotiation and Dynamical Allocation,Heterogeneity, Open Protocols and Services. Grid Environments Can Be Used Bothfor Compute Intensive Tasks and Data Intensive Applications As They Offerresources, Services, and Data Access Mechanisms. Data Mining Algorithms and Knowledge Discovery Processesare Both Compute and Data Intensive, Therefore the Grid Can Offers a Computingand Data Management Infrastructure For Supporting Decentralized and Paralleldata Analysis. This Paper Discusses How Grid Computing Can Be Used to Supportdistributed Data Mining. Grid-Based Data Mining Uses Grids As Decentralizedhigh-Performance Platforms Where to Execute Data Mining Tasks and Knowledgediscovery Algorithms and Applications. Here We Outline some Research Activitiesin Grid-Based Data Mining, some Challenges In This Area and Sketch Somepromising Future Directions For Developing Gridbased Distributed Data Mining. Data Mining Algorithms Are Widely Used Today For Theanalysis of Large Corporate and Scientific Datasets Stored In Databases Anddata Archives. Industry, Science, and Commerce Fields Often Need to Analyzevery Large ...