A New Implementation of Attribute Reduction Using Quick Relative Reduct Algorithm

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Provided by: Interscience Open Access Journals
Topic: Data Management
Format: PDF
Knowledge discovery or data mining is the process of finding previously unknown and potentially interesting patterns and relations in large databases. The so-called \"Curse of dimensionality\" pertinent to many learning algorithms, denotes the drastic increase in computational complexity and classification error with data having a great number of dimensions. Beside this problem, some individual features, being irrelevant or indirectly relevant for the learning concepts, form poor problem representation space. This paper proposes quick relative reduct algorithm to solve the attribute reduction problem in roughest theory.
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