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

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 ve...

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Main Authors: Carter, C.L., Hamilton, H.J.
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Content based retrieval
Data mining
Information retrieval
Machine learning
Memory management
Motion pictures
Pattern recognition
Relational databases
Testing
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Hamilton, H.J.
description 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.
doi_str_mv 10.1109/TAI.1995.479846
format conference_proceeding
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Computer science
Content based retrieval
Data mining
Information retrieval
Machine learning
Memory management
Motion pictures
Pattern recognition
Relational databases
Testing
title Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases
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