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An iterative strategy for pattern discovery in high-dimensional data sets
2002
Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02
High-dimensional data representation in which each data item (termed target object) is described by many features, is a necessary component of many applications. For example, in DNA microarrays, each sample (target odject) is represented by thousands of genes as features. Pattern discovery of target objects presents interesting but also very challenging problems. The data sets are typically not task-specific, many features are irrelevant or redundant and should be pruned out or filtered for the
doi:10.1145/584796.584798
fatcat:hkwzbzfbqbeshc7mnrzdatqoem