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
收藏
页码：333 / 349
页数：17
相关论文
共 50 条
  • [31] API-GNN: attribute preserving oriented interactive graph neural network
    Zhou, Yuchen
    Shang, Yanmin
    Cao, Yanan
    Li, Qian
    Zhou, Chuan
    Xu, Guandong
    [J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, 2022, 25 (01): : 239 - 258
  • [32] API-GNN: attribute preserving oriented interactive graph neural network
    Yuchen Zhou
    Yanmin Shang
    Yanan Cao
    Qian Li
    Chuan Zhou
    Guandong Xu
    [J]. World Wide Web, 2022, 25 : 239 - 258
  • [33] SIMULATION-ORIENTED NOISE MODEL FOR MOS DEVICES
    NICOLLINI, G
    PANCINI, D
    PERNICI, S
    [J]. IEEE JOURNAL OF SOLID-STATE CIRCUITS, 1987, 22 (06) : 1209 - 1212
  • [34] Acoustic noise in induction motors: Causes and solutions
    Nau, SL
    Mello, HGG
    [J]. INDUSTRY APPLICATIONS SOCIETY 47TH ANNUAL PETROLEUM AND CHEMICAL INDUSTRY CONFERENCE, 2000, : 253 - 263
  • [35] REDUCTION OF NOISE FROM INDUCTION FURNACES - 2. NOISE GENERATION MECHANISM.
    MCQUEEN, DOUGLAS H.
    [J]. 1982, V 11 (N 4): : 173 - 178
  • [36] Observer performance detecting signals in globally nonstationary oriented noise
    Zhang, Yani
    Abbey, Craig K.
    Eckstein, Miguel P.
    [J]. MEDICAL IMAGING 2006: IMAGE PERCEPTION, OBSERVER PERFORMANCE AND TECHNOLOGY ASSESSMENT, 2006, 6146
  • [37] Intrusion Detection using Attribute Subset Selector Bagging (ASUB) to Handle Imbalance and Noise
    Priya, A. Sagaya
    Kumar, S. Britto Ramesh
    [J]. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2022, 22 (05): : 97 - 102
  • [38] Attribute Balancing - Trading-Off Noise Versus Emissions, Ride and Handling and Driveability
    Tate, Simon
    Munday, Ben
    Crowther, Phil
    Maunder, Matt
    [J]. AUTOMOTIVE ACOUSTICS CONFERENCE 2019, 2020, : 83 - 98
  • [39] The noise of the electrical induction motors with squirrel - Cage rotor
    Peter, Ioan
    [J]. OPTIM 04: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT, VOL II: POWER ELECTRONICS, ELECTRICAL MACHINES AND DRIVES, 2004, : 163 - 168
  • [40] Noise generation of fluttering flag in a free stream
    Nishikawa, Reon
    Terashima, Osamu
    Konishi, Yasufumi
    Okuno, Miyu
    [J]. JOURNAL OF FLUID SCIENCE AND TECHNOLOGY, 2021, 16 (01):
12345