Knowledge Map: Toward a New Approach Supporting the Knowledge Management
  in Distributed Data Mining
  Distributed data mining (DDM) deals with the problem of finding patterns or
models, called knowledge, in an environment with distributed data and
computations. Today, a massive amounts of data which are often geographically
distributed and owned by different organisation are being mined. As
consequence, a large mount of knowledge are being produced. This causes
problems of not only knowledge management but also visualization in data
mining. Besides, the main aim of DDM is to exploit fully the benefit of
distributed data analysis while minimising the communication. Existing DDM
techniques perform partial analysis of local data at individual sites and then
generate a global model by aggregating these local results. These two steps are
not independent since naive approaches to local analysis may produce an
incorrect and ambiguous global data model. The integrating and cooperating of
these two steps need an effective knowledge management, concretely an efficient
map of knowledge in order to take the advantage of mined knowledge to guide
mining the data. In this paper, we present "knowledge map", a representation of
knowledge about mined knowledge. This new approach aims to manage efficiently
mined knowledge in large scale distributed platform such as Grid. This
knowledge map is used to facilitate not only the visualization, evaluation of
mining results but also the coordinating of local mining process and existing
knowledge to increase the accuracy of final model.
