@ARTICLE{Hung_02,
    AUTHOR      = {E. Hung and D. Cheung},
    TITLE       = {{Parallel Mining of Outliers in Large Database}},
    JOURNAL     = {{Distributed and Parallel Databases}},
    YEAR        = {2002},
    VOLUME      = {12},
    NUMBER      = {},
    PAGES       = {5--26},
    MONTH       = {July},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.cs.umd.edu/~ehung/paper/pnl2kluwer.pdf},
    ABSTRACT    = {Data mining is a new, important and fast growing database application. Outlier
                  (exception) detection is one kind of data mining, which can be applied in a variety
                  of areas like monitoring of credit card fraud and criminal activities in electronic
                  commerce. With the ever-increasing size and attributes (dimensions) of database,
                  previously proposed detection methods for two dimensions are no longer applicable.
                  The time complexity of the Nested-Loop (NL) algorithm (Knorr and Ng, in Proc. 24th
                  VLDB, 1998) is linear to the dimensionality but quadratic to the dataset size,
                  inducing an unacceptable cost for large dataset.

          A more efficient version (ENL) and its parallel version (PENL) are introduced. In
          theory, the improvement of performance in PENL is linear to the number of processors,
          as shown in a performance comparison between ENL and PENL using Bulk Synchronization
          Parallel (BSP) model. The great improvement is further verified by experiments on a
          parallel computer system IBM 9076 SP2. The results show that it is a very good choice
          to mine outliers in a cluster of workstations with a low-cost interconnected by a
          commodity communication network.},
}