Noise-free attribute-oriented induction

被引：0

作者：

Hu, Hsiao-Wei ^{[1
,3
]}

Chen, Yen-Liang ^{[2
,4
]}

Hong, Jia-Yu ^{[2
,4
]}

机构：

[1] Soo Chow Univ, Sch Big Data Management, Taipei, Taiwan

[2] Natl Cent Univ, Dept Informat Management, Chungli, Taiwan

[3] 70 Linshi Rd, Taipei 111, Taiwan

[4] 300 Jhongda Rd, Taoyuan 32001, Taiwan

来源：

INFORMATION SCIENCES | 2021年 / 568卷

关键词：

Data mining; Attribute-Oriented Induction; Concept Hierarchy; Noise; KNOWLEDGE DISCOVERY;

D O I：

10.1016/j.ins.2021.04.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Attribute-oriented induction (AOI) was originally developed to facilitate the mining of generalized knowledge in relational databases. Input data for the AOI method comprises a relational table and a concept tree for each attribute. The output is a small relation that contains a number of generalized tuples which summarize the general characteristics of the relational table. Ideally, the generalized tuples shown in the induction table represent the patterns of information that appear in the table. However, if the input data contains a large amount of noise, the generalized tuples may contain too little information to be useful. Existing research into AOI has yet to focus on the elimination of noise. To fill this gap, we developed two noise-free AOI algorithms that filter out noise to enhance the specificity of AOI results. (c) 2021 Elsevier Inc. All rights reserved.

引用

页码：333 / 349

页数：17

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