Abstract
The function of data reduction is to make data sets smaller, while preserving classification structures of interest. A novel approach to data reduction based on spatial partitioning is proposed in this paper. This algorithm projects conventional database relations into multidimensional data space. The advantage of this approach is to change the data reduction process into a spatial merging process of data in the same class, as well as a spatial partitioning process of data in different classes, in multidimensional data space. A series of partitioned regions are eventually obtained and can easily be used in data classification. The proposed method was evaluated using 7 real world data sets. The results were quite remarkable compared with those obtained by C4.5 and DR. The efficiency of the proposed algorithm was better than DR without loss of test accuracy and reduction ratio.
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