A dynamic and scalable evolutionary data mining and kdd for distributed environment

Abstract

Due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge The heterogeneous nature of data resultant models and techniques raise problems on how to generalize knowledge in order to have global view of the phenomena across the entire organization The centralized and intermediate message passing approaches are not much efficient due to Storage cost Communication cost Computational cost Private and sensitive data The proposed framework and the algorithm is much efficient and fulfil the objectives such as to minimize the training time and communication overhead and to preserve the prediction quality newline