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Handling Large Databases in Data Mining
Abstract
Current database technology involves processing a large volume of data in order to discover new knowledge. The high volume of data makes discovery process computationally expensive. In addition, real-world databases tend to be incomplete, redundant, and inconsistent that could lead to discovering redundant and inconsistent knowledge. We propose to use domain knowledge to reduce the size of the database being considered for discovery and to optimize the hypothesis (representing the pattern to be discovered) by eliminating implied, unnecessary, and redundant conditions from the hypothesis. The benefits can be greater efficiency and the discovery of more meaningful, non-redundant, non-trivial, and consistent rules.
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