Fast outlier data mining algorithm based on cell in large datasets
Gou Guang-le · Journal of Chongqing University of Posts and Telecommunications · 2010
The paper proposed a fast cell-based algorithm for outlier detection in large datasets(short for FOMABCLD).The algorithm applied cluster technique to preprocesse data,and placed data into the appropriate cells based on their values and indexed the non-empty cells with Cell Dimension-Tree.A majority part of data located in high density cells and had no nearness relationship with outliers is filtered,which avoided large useless computations.The experiment show that FOMABCLD can mine outlier data from large datasets fast and accurately,and the speed of detecting outliers is increased.