Record reduction based on attribute oriented generalization

被引：0

作者：

Wang, LZ ^{[1
]}

Chen, HM ^{[1
]}

机构：

[1] Yunnan Univ, Dept Comp Sci & Engn, Sch Informat, Kunming 650091, Peoples R China

来源：

Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 | 2005年

关键词：

record reduction; attribute-oriented generalization; information amount based on semantic proximity; record reduction based on attribute-oriented generalization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Record reduction is very important in the research and application of KDD. The aim of record reduction is to keep less record count and more information amount. Ratio of Record Reduction (RRR) and Information Amount Based on Semantic Proximity (IABSP) are presented as measures. Record reduction is analyzed from two aspects of rules and measures in order to ensure the correction and effectiveness of results. In this paper, record reduction is materialized as Record Reduction Based on Attribute Oriented Generalization (RRBAOG). A new AOG method based on partition, prune and optimization strategies is presented in order to improve the execution efficiency of RRBAOG. Two algorithms of RRBAOG, From Bottom to Top (FBTT) and From Top to Bottom (FTTB) are also given. The efficiency of algorithms is analyzed by experiments.

引用

页码：1693 / 1700

页数：8

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