@ARTICLE{Chen_04,
    AUTHOR      = {R. Chen and K. Sivakumar and H. Kargupta},
    TITLE       = {{Collective Mining of Bayesian Networks from Distributed Heterogeneous Data}},
    JOURNAL     = {{Knowledge and Information Systems}},
    YEAR        = {2004},
    VOLUME      = {6},
    NUMBER      = {2},
    PAGES       = {164-187},
    MONTH       = {March},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.cs.umbc.edu/~hillol/PUBS/kais02.pdf},
    ABSTRACT    = {We present a collective approach to learning a Bayesian network from distributed
               heterogeneous data. In this approach, we first learn a local Bayesian network at
               each site using the local data. Then each site identifies the observations that are
               most likely to be evidence of coupling between local and non-local variables and
               transmits a subset of these observations to a central site. Another Bayesian
               network is learnt at the central site using the data transmitted from the local
               site. The local and central Bayesian networks are combined to obtain a collective
               Bayesian network, which models the entire data. Experimental results and theoretical
               justification that demonstrate the feasibility of our approach are presented},
}