Now showing 1 - 2 of 2
  • Publication
    Distributed Knowledge Map for Mining Data on Grid Platforms
    Recently, huge datasets representing different applications domains are produced and stored on distributed platforms. These datasets are, generally, owned by different organizations. As a consequence, The scale and distribution nature of these datasets have created the problem of efficient mining and management on these platforms. Most of the existing knowledge management approaches are mainly for centralized data mining. Few of them propose solutions for mining and handling knowledge on Grid. However, the new knowledge is stored and managed as any other kinds of resources.
  • Publication
    Variance-based Clustering Technique for Distributed Data Mining Applications
    Nowadays, huge amounts of data are naturally collected in distributed sites due to different facts and moving these data through the network for extracting useful knowledge is almost unfeasible for either technical reasons or policies. Furthermore, classical parallel algorithms cannot be applied, specially in loosely coupled environments. This requires to develop scalable distributed algorithms able to return the global knowledge by aggregating local results in an effective way. In this paper we propose a distributed algorithm based on independent local clustering processes and a global merging based on minimum variance increases and requires a limited communication overhead. We also introduce the notion of distributed sub-clusters perturbation to improve the global generated distribution. We show that this algorithm improves the quality of clustering compared to classical local centralized ones and is able to find real global data nature or distribution.