Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases

Authors:
C. L. Carter;H. J. Hamilton
Affiliations:
-;-
Venue:
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Year:
1995

Citing 0
Cited 5

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Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases

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ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I

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Abstract

Practical tools for knowledge discovery from databases must be efficient enough to handle large data sets found in commercial environments. Attribute-oriented induction has proved to be a useful method for knowledge discovery. Three algorithms are AOI, LCHR and GDBR. We have implemented efficient versions of each algorithm and empirically compared them on large commercial data sets. These tests show that GDBR is consistently faster than AOI and LCHR. GDBR's times increase linearly with increased input size, while times for AOI and LCHR increase non-linearly when memory is exceeded. Through better memory management, however, AOI can be improved to provide some advantages.